Articles of manufacture and methods for modeling Saccharomyces cerevisiae metabolism

ABSTRACT

The invention provides an in silico model for determining a  S. cerevisiae  physiological function. The model includes a data structure relating a plurality of  S. cerevisiae  reactants to a plurality of  S. cerevisiae  reactions, a constraint set for the plurality of  S. cerevisiae  reactions, and commands for determining a distribution of flux through the reactions that is predictive of a  S. cerevisiae  physiological function. A model of the invention can further include a gene database containing information characterizing the associated gene or genes. The invention further provides methods for making an in silico  S. cerevisiae  model and methods for determining a  S. cerevisiae  physiological function using a model of the invention.

This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application No. 60/344,447 filed Oct. 26, 2001, which is incorporated herein by reference in its entirety.

This invention was made with United States Government support under grant NIH ROIHL59234 awarded by the National Institutes of Health. The U.S. Government has certain rights in this invention.

BACKGROUND OF THE INVENTION

This invention relates generally to analysis of the activity of a chemical reaction network and, more specifically, to computational methods for simulating and predicting the activity of Saccharomyces cerevisiae (S. cerevisiae) reaction networks.

Saccharomyces cerevisiae is one of the best-studied microorganisms and in addition to its significant industrial importance it serves as a model organism for the study of eukaryotic cells (Winzeler et al. Science 285: 901-906 (1999)). Up to 30% of positionally cloned genes implicated in human disease have yeast homologs.

The first eukaryotic genome to be sequenced was that of S. cerevisiae, and about 6400 open reading frames (or genes) have been identified in the genome. S. cerevisiae was the subject of the first expression profiling experiments and a compendium of expression profiles for many different mutants and different growth conditions has been established. Furthermore, a protein-protein interaction network has been defined and used to study the interactions between a large number of yeast proteins.

S. cerevisiae is used industrially to produce fuel ethanol, technical ethanol, beer, wine, spirits and baker's yeast, and is used as a host for production of many pharmaceutical proteins (hormones and vaccines). Furthermore, S. cerevisiae is currently being exploited as a cell factory for many different bioproducts including insulin.

Genetic manipulations, as well as changes in various fermentation conditions, are being considered in an attempt to improve the yield of industrially important products made by S. cerevisiae. However, these approaches are currently not guided by a clear understanding of how a change in a particular parameter, or combination of parameters, is likely to affect cellular behavior, such as the growth of the organism, the production of the desired product or the production of unwanted by-products. It would be valuable to be able to predict how changes in fermentation conditions, such as an increase or decrease in the supply of oxygen or a media component, would affect cellular behavior and, therefore, fermentation performance. Likewise, before engineering the organism by addition or deletion of one or more genes, it would be useful to be able to predict how these changes would affect cellular behavior.

However, it is currently difficult to make these sorts of predictions for S. cerevisiae because of the complexity of the metabolic reaction network that is encoded by the S. cerevisiae genome. Even relatively minor changes in media composition can affect hundreds of components of this network such that potentially hundreds of variables are worthy of consideration in making a prediction of fermentation behavior. Similarly, due to the complexity of interactions in the network, mutation of even a single gene can have effects on multiple components of the network. Thus, there exists a need for a model that describes S. cerevisiae reaction networks, such as its metabolic network, which can be used to simulate many different aspects of the cellular behavior of S. cerevisiae under different conditions. The present invention satisfies this need, and provides related advantages as well.

SUMMARY OF THE INVENTION

The invention provides a computer readable medium or media, including: (a) a data structure relating a plurality of reactants in S. cerevisiae to a plurality of reactions in S. cerevisiae, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product, (b) a constraint set for the plurality of S. cerevisiae reactions, and (c) commands for determining at least one flux distribution that minimizes or maximizes an objective function when the constraint set is applied to the data representation, wherein at least one flux distribution is predictive of a physiological function of S. cerevisiae. In one embodiment, at least one of the cellular reactions in the data structure is annotated to indicate an associated gene and the computer readable medium or media further includes a gene database including information characterizing the associated gene. In another embodiment, at least one of the cellular reactions in the data structure is annotated with an assignment of function within a subsystem or a compartment within the cell.

The invention also provides a method for predicting physiological function of S. cerevisiae, including: (a) providing a data structure relating a plurality of S. cerevisiae to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function. In one embodiment, at least one of the S. cerevisiae reactions in the data structure is annotated to indicate an associated gene and the method predicts a S. cerevisiae physiological function related to the gene.

Also provided by the invention is a method for making a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions in a computer readable medium or media, including: (a) identifying a plurality of S. cerevisiae reactions and a plurality of reactants that are substrates and products of the reactions; (b) relating the plurality of reactants to the plurality of reactions in a data structure, wherein each of the reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (c) determining a constraint set for the plurality of S. cerevisiae reactions; (d) providing an objective function; (e) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, and (f) if at least one flux distribution is not predictive of a physiological function of S. cerevisiae, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if at least one flux distribution is predictive of a physiological function of the eukaryotic cell, then storing the data structure in a computer readable medium or media. The invention further provides a data structure relating a plurality of S. cerevisiae reactants to a plurality of reactions, wherein the data structure is produced by the method.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic representation of a hypothetical metabolic network.

FIG. 2 shows the stoichiometric matrix (S) for the hypothetical metabolic network shown in FIG. 1.

FIG. 3 shows mass balance constraints and flux constraints (reversibility constraints) that can be placed on the hypothetical metabolic network shown in FIG. 1. (∞, infinity; Y₁, uptake rate value)

FIG. 4 shows an exemplary metabolic reaction network in S. cerevisiae.

FIG. 5 shows a method for reconstruction of the metabolic network of S. cerevisiae. Based on the available information from the genome annotation, biochemical pathway databases, biochemistry textbooks and recent publications, a genome-scale metabolic network for S. cerevisiae was designed. Additional physiological constraints were considered and modeled, such as growth, non-growth dependent ATP requirements and biomass composition.

FIG. 6 shows a Phenotypic Phase Plane (PhPP) diagram for S. cerevisiae revealing a finite number of qualitatively distinct patterns of metabolic pathway utilization divided into discrete phases. The characteristics of these distinct phases are interpreted using ratios of shadow prices in the form of isoclines. The isoclines can be used to classify these phases into futile, single and dual substrate limitation and to define the line of optimality. The upper part of the figure shows a 3-dimensional S. cerevisiae Phase Plane diagram. The bottom part shows a 2-dimensional Phase Plane diagram with the line of optimality (LO) indicated.

FIG. 7 shows the respiratory quotient (RQ) versus oxygen uptake rate (mmole/g-DW/hr) (upper left) on the line of optimality. The phenotypic phase plane (PhPP) illustrates that the predicted RQ is a constant of value 1.06

FIG. 8 shows phases of metabolic phenotype associated with varying oxygen availability, from completely anaerobic fermentation to aerobic growth in S. cerevisiae. The glucose uptake rate was fixed under all conditions, and the resulting optimal biomass yield, as well as respiratory quotient, RQ, are indicated along with the output fluxes associated with four metabolic by-products: acetate, succinate, pyruvate, and ethanol.

FIG. 9 shows anaerobic glucose limited continuous culture of S. cerevisiae. FIG. 9 shows the utilization of glucose at varying dilution rates in anaerobic chemostat culture. The data-point at the dilution rate of 0.0 is extrapolated from the experimental results. The shaded area or the infeasible region contains a set of stoichiometric constraints that cannot be balanced simultaneously with growth demands. The model produces the optimal glucose uptake rate for a given growth rate on the line of optimal solution (indicated by Model (optimal)). Imposition of additional constraints drives the solution towards a region where more glucose is needed (i.e. region of alternative sub-optimal solution). At the optimal solution, the in silico model does not secrete pyruvate and acetate. The maximum difference between the model and the experimental points is 8% at the highest dilution rate. When the model is forced to produce these by-products at the experimental level (Model (forced)), the glucose uptake rate is increased and becomes closer to the experimental values. FIGS. 9B and 9C show the secretion rate of anaerobic by-products in chemostat culture. (q, secretion rate; D, dilution rate).

FIG. 10 shows aerobic glucose-limited continuous culture of S. cerevisiae in vivo and in silico. FIG. 10A shows biomass yield (Y_(X)), and secretion rates of ethanol (Eth), and glycerol (Gly). FIG. 10B shows CO₂ secretion rate (q_(CO2)) and respiratory quotient (RQ; i.e. q_(CO2)/q_(O2)) of the aerobic glucose-limited continuous culture of S. cerevisiae. (exp, experimental).

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides an in silico model of the baker's and brewer's yeast, S. cerevisiae, that describes the interconnections between the metabolic genes in the S. cerevisiae genome and their associated reactions and reactants. The model can be used to simulate different aspects of the cellular behavior of S. cerevisiae under different environmental and genetic conditions, thereby providing valuable information for industrial and research applications. An advantage of the model of the invention is that it provides a holistic approach to simulating and predicting the metabolic activity of S. cerevisiae.

As an example, the S. cerevisiae metabolic model can be used to determine the optimal conditions for fermentation performance, such as for maximizing the yield of a specific industrially important enzyme. The model can also be used to calculate the range of cellular behaviors that S. cerevisiae can display as a function of variations in the activity of one gene or multiple genes. Thus, the model can be used to guide the organismal genetic makeup for a desired application. This ability to make predictions regarding cellular behavior as a consequence of altering specific parameters will increase the speed and efficiency of industrial development of S. cerevisiae strains and conditions for their use.

The S. cerevisiae metabolic model can also be used to predict or validate the assignment of particular biochemical reactions to the enzyme-encoding genes found in the genome, and to identify the presence of reactions or pathways not indicated by current genomic data. Thus, the model can be used to guide the research and discovery process, potentially leading to the identification of new enzymes, medicines or metabolites of commercial importance.

The models of the invention are based on a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product.

As used herein, the term “S. cerevisiae reaction” is intended to mean a conversion that consumes a substrate or forms a product that occurs in or by a viable strain of S. cerevisiae. The term can include a conversion that occurs due to the activity of one or more enzymes that are genetically encoded by a S. cerevisiae genome. The term can also include a conversion that occurs spontaneously in a S. cerevisiae cell. Conversions included in the term include, for example, changes in chemical composition such as those due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, glycolysation, reduction, oxidation or changes in location such as those that occur due to a transport reaction that moves a reactant within the same compartment or from one cellular compartment to another. In the case of a transport reaction, the substrate and product of the reaction can be chemically the same and the substrate and product can be differentiated according to location in a particular cellular compartment. Thus, a reaction that transports a chemically unchanged reactant from a first compartment to a second compartment has as its substrate the reactant in the first compartment and as its product the reactant in the second compartment. It will be understood that when used in reference to an in silico model or data structure, a reaction is intended to be a representation of a chemical conversion that consumes a substrate or produces a product.

As used herein, the term “S. cerevisiae reactant” is intended to mean a chemical that is a substrate or a product of a reaction that occurs in or by a viable strain of S. cerevisiae. The term can include substrates or products of reactions performed by one or more enzymes encoded by S. cerevisiae gene(s), reactions occurring in S. cerevisiae that are performed by one or more non-genetically encoded macromolecule, protein or enzyme, or reactions that occur spontaneously in a S. cerevisiae cell. Metabolites are understood to be reactants within the meaning of the term. It will be understood that when used in reference to an in silico model or data structure, a reactant is intended to be a representation of a chemical that is a substrate or a product of a reaction that occurs in or by a viable strain of S. cerevisiae.

As used herein the term “substrate” is intended to mean a reactant that can be converted to one or more products by a reaction. The term can include, for example, a reactant that is to be chemically changed due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, reduction, oxidation or that is to change location such as by being transported across a membrane or to a different compartment.

As used herein, the term “product” is intended to mean a reactant that results from a reaction with one or more substrates. The term can include, for example, a reactant that has been chemically changed due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, reduction or oxidation or that has changed location such as by being transported across a membrane or to a different compartment.

As used herein, the term “stoichiometric coefficient” is intended to mean a numerical constant correlating the number of one or more reactants and the number of one or more products in a chemical reaction. Typically, the numbers are integers as they denote the number of molecules of each reactant in an elementally balanced chemical equation that describes the corresponding conversion. However, in some cases the numbers can take on non-integer values, for example, when used in a lumped reaction or to reflect empirical data.

As used herein, the term “plurality,” when used in reference to S. cerevisiae reactions or reactants is intended to mean at least 2 reactions or reactants. The term can include any number of S. cerevisiae reactions or reactants in the range from 2 to the number of naturally occurring reactants or reactions for a particular strain of S. cerevisiae. Thus, the term can include, for example, at least 10, 20, 30, 50, 100, 150, 200, 300, 400, 500, 600 or more reactions or reactants. The number of reactions or reactants can be expressed as a portion of the total number of naturally occurring reactions for a particular strain of S. cerevisiae such as at least 20%, 30%, 50%, 60%, 75%, 90%, 95% or 98% of the total number of naturally occurring reactions that occur in a particular strain of S. cerevisiae.

As used herein, the term “data structure” is intended to mean a physical or logical relationship among data elements, designed to support specific data manipulation functions. The term can include, for example, a list of data elements that can be added combined or otherwise manipulated such as a list of representations for reactions from which reactants can be related in a matrix or network. The term can also include a matrix that correlates data elements from two or more lists of information such as a matrix that correlates reactants to reactions. Information included in the term can represent, for example, a substrate or product of a chemical reaction, a chemical reaction relating one or more substrates to one or more products, a constraint placed on a reaction, or a stoichiometric coefficient.

As used herein, the term “constraint” is intended to mean an upper or lower boundary for a reaction. A boundary can specify a minimum or maximum flow of mass, electrons or energy through a reaction. A boundary can further specify directionality of a reaction. A boundary can be a constant value such as zero, infinity, or a numerical value such as an integer and non-integer.

As used herein, the term “activity,” when used in reference to a reaction, is intended to mean the rate at which a product is produced or a substrate is consumed. The rate at which a product is produced or a substrate is consumed can also be referred to as the flux for the reaction.

As used herein, the term “activity,” when used in reference to S. cerevisiae is intended to mean the rate of a change from an initial state of S. cerevisiae to a final state of S. cerevisiae. The term can include, the rate at which a chemical is consumed or produced by S. cerevisiae, the rate of growth of S. cerevisiae or the rate at which energy or mass flow through a particular subset of reactions.

The invention provides a computer readable medium, having a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product.

The plurality of S. cerevisiae reactions can include reactions of a peripheral metabolic pathway. As used herein, the term “peripheral,” when used in reference to a metabolic pathway, is intended to mean a metabolic pathway that includes one or more reactions that are not a part of a central metabolic pathway. As used herein, the term “central,” when used in reference to a metabolic pathway, is intended to mean a metabolic pathway selected from glycolysis, the pentose phosphate pathway (PPP), the tricarboxylic acid (TCA) cycle and the electron transfer system (ETS), associated anapleurotic reactions, and pyruvate metabolism.

A plurality of S. cerevisiae reactants can be related to a plurality of S. cerevisiae reactions in any data structure that represents, for each reactant, the reactions by which it is consumed or produced. Thus, the data structure, which is referred to herein as a “reaction network data structure,” serves as a representation of a biological reaction network or system. An example of a reaction network that can be represented in a reaction network data structure of the invention is the collection of reactions that constitute the metabolic reactions of S. cerevisiae.

The methods and models of the invention can be applied to any strain of S. cerevisiae including, for example, strain CEN.PK113.7D or any laboratory or production strain. A strain of S. cerevisiae can be identified according to classification criteria known in the art. Classification criteria include, for example, classical microbiological characteristics, such as those upon which taxonomic classification is traditionally based, or evolutionary distance as determined for example by comparing sequences from within the genomes of organisms, such as ribosome sequences.

The reactants to be used in a reaction network data structure of the invention can be obtained from or stored in a compound database. As used herein, the term “compound database” is intended to mean a computer readable medium or media containing a plurality of molecules that includes substrates and products of biological reactions. The plurality of molecules can include molecules found in multiple organisms, thereby constituting a universal compound database. Alternatively, the plurality of molecules can be limited to those that occur in a particular organism, thereby constituting an organism-specific compound database. Each reactant in a compound database can be identified according to the chemical species and the cellular compartment in which it is present. Thus, for example, a distinction can be made between glucose in the extracellular compartment versus glucose in the cytosol. Additionally each of the reactants can be specified as a metabolite of a primary or secondary metabolic pathway. Although identification of a reactant as a metabolite of a primary or secondary metabolic pathway does not indicate any chemical distinction between the reactants in a reaction, such a designation can assist in visual representations of large networks of reactions.

As used herein, the term “compartment” is intended to mean a subdivided region containing at least one reactant, such that the reactant is separated from at least one other reactant in a second region. A subdivided region included in the term can be correlated with a subdivided region of a cell. Thus, a subdivided region included in the term can be, for example, the intracellular space of a cell; the extracellular space around a cell; the periplasmic space; the interior space of an organelle such as a mitochondrium, endoplasmic reticulum, Golgi apparatus, vacuole or nucleus; or any subcellular space that is separated from another by a membrane or other physical barrier. Subdivided regions can also be made in order to create virtual boundaries in a reaction network that are not correlated with physical barriers. Virtual boundaries can be made for the purpose of segmenting the reactions in a network into different compartments or substructures.

As used herein, the term “substructure” is intended to mean a portion of the information in a data structure that is separated from other information in the data structure such that the portion of information can be separately manipulated or analyzed. The term can include portions subdivided according to a biological function including, for example, information relevant to a particular metabolic pathway such as an internal flux pathway, exchange flux pathway, central metabolic pathway, peripheral metabolic pathway, or secondary metabolic pathway. The term can include portions subdivided according to computational or mathematical principles that allow for a particular type of analysis or manipulation of the data structure.

The reactions included in a reaction network data structure can be obtained from a metabolic reaction database that includes the substrates, products, and stoichiometry of a plurality of metabolic reactions of S. cerevisiae. The reactants in a reaction network data structure can be designated as either substrates or products of a particular reaction, each with a stoichiometric coefficient assigned to it to describe the chemical conversion taking place in the reaction. Each reaction is also described as occurring in either a reversible or irreversible direction. Reversible reactions can either be represented as one reaction that operates in both the forward and reverse direction or be decomposed into two irreversible reactions, one corresponding to the forward reaction and the other corresponding to the backward reaction.

Reactions included in a reaction network data structure can include intra-system or exchange reactions. Intra-system reactions are the chemically and electrically balanced interconversions of chemical species and transport processes, which serve to replenish or drain the relative amounts of certain metabolites. These intra-system reactions can be classified as either being transformations or translocations. A transformation is a reaction that contains distinct sets of compounds as substrates and products, while a translocation contains reactants located in different compartments. Thus, a reaction that simply transports a metabolite from the extracellular environment to the cytosol, without changing its chemical composition is solely classified as a translocation, while a reaction such as the phosphotransferase system (PTS) which takes extracellular glucose and converts it into cytosolic glucose-6-phosphate is a translocation and a transformation.

Exchange reactions are those which constitute sources and sinks, allowing the passage of metabolites into and out of a compartment or across a hypothetical system boundary. These reactions are included in a model for simulation purposes and represent the metabolic demands placed on S. cerevisiae. While they may be chemically balanced in certain cases, they are typically not balanced and can often have only a single substrate or product. As a matter of convention the exchange reactions are further classified into demand exchange and input/output exchange reactions.

The metabolic demands placed on the S. cerevisiae metabolic reaction network can be readily determined from the dry weight composition of the cell which is available in the published literature or which can be determined experimentally. The uptake rates and maintenance requirements for S. cerevisiae can be determined by physiological experiments in which the uptake rate is determined by measuring the depletion of the substrate. The measurement of the biomass at each point can also be determined, in order to determine the uptake rate per unit biomass. The maintenance requirements can be determined from a chemostat experiment. The glucose uptake rate is plotted versus the growth rate, and the y-intercept is interpreted as the non-growth associated maintenance requirements. The growth associated maintenance requirements are determined by fitting the model results to the experimentally determined points in the growth rate versus glucose uptake rate plot.

Input/output exchange reactions are used to allow extracellular reactants to enter or exit the reaction network represented by a model of the invention. For each of the extracellular metabolites a corresponding input/output exchange reaction can be created. These reactions can either be irreversible or reversible with the metabolite indicated as a substrate with a stoichiometric coefficient of one and no products produced by the reaction. This particular convention is adopted to allow the reaction to take on a positive flux value (activity level) when the metabolite is being produced or removed from the reaction network and a negative flux value when the metabolite is being consumed or introduced into the reaction network. These reactions will be further constrained during the course of a simulation to specify exactly which metabolites are available to the cell and which can be excreted by the cell.

A demand exchange reaction is always specified as an irreversible reaction containing at least one substrate. These reactions are typically formulated to represent the production of an intracellular metabolite by the metabolic network or the aggregate production of many reactants in balanced ratios such as in the representation of a reaction that leads to biomass formation, also referred to as growth. As set forth in the Examples, the biomass components to be produced for growth include L-Alanine, L-Arginine, L-Asparagine, L-Aspartate, L-Cysteine, L-Glutamine, L-Glutamate, Glycine, L-Histidine, L-Isoleucine, L-Leucine, L-Lysine, L-Methionine, L-Phenylalanine, L-Proline, L-Serine, L-Threonine, L-Tryptophan, L-Tyrosine, L-Valine, AMP, GMP, CMP, UMP, dAMP, dCMP, dTMP, dGMP, Glycogen, alpha,alpha-Trehalose, Mannan, beta-D-Glucan, Triacylglycerol, Ergosterol, Zymosterol, Phosphatidate, Phosphatidylcholine, Phosphatidylethanolamine, Phosphatidyl-D-myo-inositol, Phosphatidylserine, ATP, Sulfate, ADP and Orthophosphate, with exemplary values shown in Table 1.

TABLE 1 Cellular components of S. cerevisiae (mmol/gDW). ALA 0.459 CMP 0.05 ARG 0.161 dAMP 0.0036 ASN 0.102 dCMP 0.0024 ASP 0.297 dGMP 0.0024 CYS 0.007 DTMP 0.0036 GLU 0.302 TAGLY 0.007 GLN 0.105 ERGOST 0.0007 GLY 0.290 ZYMST 0.015 HIS 0.066 PA 0.0006 ILE 0.193 PINS 0.005 LEU 0.296 PS 0.002 LYS 0.286 PE 0.005 MET 0.051 PC 0.006 PHE 0.134 GLYCOGEN 0.519 PRO 0.165 TRE 0.023 SER 0.185 Mannan 0.809 THR 0.191 13GLUCAN 1.136 TRP 0.028 SLF 0.02 TYR 0.102 ATP 23.9166 VAL 0.265 ADP 23.9166 AMP 0.051 PI 23.9456 GMP 0.051 Biomass 1 UMP 0.067

A demand exchange reaction can be introduced for any metabolite in a model of the invention. Most commonly, these reactions are introduced for metabolites that are required to be produced by the cell for the purposes of creating a new cell such as amino acids, nucleotides, phospholipids, and other biomass constituents, or metabolites that are to be produced for alternative purposes. Once these metabolites are identified, a demand exchange reaction that is irreversible and specifies the metabolite as a substrate with a stoichiometric coefficient of unity can be created. With these specifications, if the reaction is active it leads to the net production of the metabolite by the system meeting potential production demands. Examples of processes that can be represented as a demand exchange reaction in a reaction network data structure and analyzed by the methods of the invention include, for example, production or secretion of an individual protein; production or secretion of an individual metabolite such as an amino acid, vitamin, nucleoside, antibiotic or surfactant; production of ATP for extraneous energy requiring processes such as locomotion; or formation of biomass constituents.

In addition to these demand exchange reactions that are placed on individual metabolites, demand exchange reactions that utilize multiple metabolites in defined stoichiometric ratios can be introduced. These reactions are referred to as aggregate demand exchange reactions. An example of an aggregate demand reaction is a reaction used to simulate the concurrent growth demands or production requirements associated with cell growth that are placed on a cell, for example, by simulating the formation of multiple biomass constituents simultaneously at a particular cellular growth rate.

A hypothetical reaction network is provided in FIG. 1 to exemplify the above-described reactions and their interactions. The reactions can be represented in the exemplary data structure shown in FIG. 2 as set forth below. The reaction network, shown in FIG. 1, includes intrasystem reactions that occur entirely within the compartment indicated by the shaded oval such as reversible reaction R₂ which acts on reactants B and G and reaction R₃ which converts one equivalent of B to two equivalents of F. The reaction network shown in FIG. 1 also contains exchange reactions such as input/output exchange reactions A_(xt) and E_(xt), and the demand exchange reaction, V_(growth), which represents growth in response to the one equivalent of D and one equivalent of F. Other intrasystem reactions include R₁ which is a translocation and transformation reaction that translocates reactant A into the compartment and transforms it to reactant G and reaction R₆ which is a transport reaction that translocates reactant E out of the compartment.

A reaction network can be represented as a set of linear algebraic equations which can be presented as a stoichiometric matrix S, with S being an m×n matrix where m corresponds to the number of reactants or metabolites and n corresponds to the number of reactions taking place in the network. An example of a stoichiometric matrix representing the reaction network of FIG. 1 is shown in FIG. 2. As shown in FIG. 2, each column in the matrix corresponds to a particular reaction n, each row corresponds to a particular reactant m, and each S_(mn) element corresponds to the stoichiometric coefficient of the reactant m in the reaction denoted n. The stoichiometric matrix includes intra-system reactions such as R₂ and R₃ which are related to reactants that participate in the respective reactions according to a stoichiometric coefficient having a sign indicative of whether the reactant is a substrate or product of the reaction and a value correlated with the number of equivalents of the reactant consumed or produced by the reaction. Exchange reactions such as -E_(xt) and -A_(xt) are similarly correlated with a stoichiometric coefficient. As exemplified by reactant E, the same compound can be treated separately as an internal reactant (E) and an external reactant (E_(external)) such that an exchange reaction (R₆) exporting the compound is correlated by stoichiometric coefficients of −1 and 1, respectively. However, because the compound is treated as a separate reactant by virtue of its compartmental location, a reaction, such as R₅, which produces the internal reactant (E) but does not act on the external reactant (E_(external)) is correlated by stoichiometric coefficients of 1 and 0, respectively. Demand reactions such as V_(growth) can also be included in the stoichiometric matrix being correlated with substrates by an appropriate stoichiometric coefficient.

As set forth in further detail below, a stoichiometric matrix provides a convenient format for representing and analyzing a reaction network because it can be readily manipulated and used to compute network properties, for example, by using linear programming or general convex analysis. A reaction network data structure can take on a variety of formats so long as it is capable of relating reactants and reactions in the manner exemplified above for a stoichiometric matrix and in a manner that can be manipulated to determine an activity of one or more reactions using methods such as those exemplified below. Other examples of reaction network data structures that are useful in the invention include a connected graph, list of chemical reactions or a table of reaction equations.

A reaction network data structure can be constructed to include all reactions that are involved in S. cerevisiae metabolism or any portion thereof. A portion of S. cerevisiae metabolic reactions that can be included in a reaction network data structure of the invention includes, for example, a central metabolic pathway such as glycolysis, the TCA cycle, the PPP or ETS; or a peripheral metabolic pathway such as amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, vitamin or cofactor biosynthesis, transport processes and alternative carbon source catabolism. Examples of individual pathways within the peripheral pathways are set forth in Table 2, including, for example, the cofactor biosynthesis pathways for quinone biosynthesis, riboflavin biosynthesis, folate biosyntheis, coenzyme A biosynthesis, NAD biosynthesis, biotin biosynthesis and thiamin biosynthesis.

Depending upon a particular application, a reaction network data structure can include a plurality of S. cerevisiae reactions including any or all of the reactions listed in Table 2. Exemplary reactions that can be included are those that are identified as being required to achieve a desired S. cerevisiae specific growth rate or activity including, for example, reactions identified as ACO1, CDC19, CIT1, DAL7, ENO1, FBA1, FBP1, FUM1, GND1, GPM1, HXK1, ICL1, IDH1, IDH2, IDP1, IDP2, IDP3, KGD1, KGD2, LPD1, LSC1, LSC2, MDH1, MDH2, MDH3, MLS1, PDC1, PFK1, PFK2, PGI1, PGK1, PGM1, PGM2, PYC1, PYC2, PYK2, RKI1, RPE1, SOL1, TAL1, TDH1, TDH2, TDH3, TKL1, TPI1, ZWF1 in Table 2. Other reactions that can be included are those that are not described in the literature or genome annotation but can be identified during the course of iteratively developing a S. cerevisiae model of the invention including, for example, reactions identified as MET6_(—)2, MNADC, MNADD1, MNADE, MNADF_(—)1, MNADPHPS, MNADG1, MNADG2, MNADH, MNPT1.

TABLE 2 Locus # E.C. # Gene Gene Description Reaction Rxn Name Carbohydrate Metabolism Glycolysis/Gluconeogenesis YCL040W 2.7.1.2 GLK1 Glucokinase GLC + ATP -> G6P + ADP glk1_1 YCL040W 2.7.1.2 GLK1 Glucokinase MAN + ATP -> MAN6P + ADP glk1_2 YCL040W 2.7.1.2 GLK1 Glucokinase bDGLC + ATP -> bDG6P + ADP glk1_3 YFR053C 2.7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) bDGLC + ATP -> G6P + ADP hxk1_1 YFR053C 2.7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) GLC + ATP -> G6P + ADP hxk1_2 YFR053C 2.7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) MAN + ATP -> MAN6P + ADP hxk1_3 YFR053C 2.7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) ATP + FRU -> ADP + F6P hxk1_4 YGL253W 2.7.1.1 HXK2 Hexokinase II (PII) (also called Hexokinase B) bDGLC + ATP -> G6P + ADP hxk2_1 YGL253W 2.7.1.1 HXK2 Hexokinase II (PII) (also called Hexokinase B) GLC + ATP -> G6P + ADP hxk2_2 YGL253W 2.7.1.1 HXK2 Hexokinase II (PII) (also called Hexokinase B) MAN + ATP -> MAN6P + ADP hxk2_3 YGL253W 2.7.1.1 HXK2 Hexokinase II (PII) (also called Hexokinase B) ATP + FRU -> ADP + F6P hxk2_4 YBR196C 5.3.1.9 PGI1 Glucose-6-phosphate isomerase G6P <-> F6P pgi1_1 YBR196C 5.3.1.9 PGI1 Glucose-6-phosphate isomerase G6P <-> bDG6P pgi1_2 YBR196C 5.3.1.9 PGI1 Glucose-6-phosphate isomerase bDG6P <-> F6P pgi1_3 YMR205C 2.7.1.11 PFK2 phosphofructokinase beta subunit F6P + ATP -> FDP + ADP pfk2 YGR240C 2.7.1.11 PFK1 phosphofructokinase alpha subunit F6P + ATP -> FDP + ADP pfk1_1 YGR240C 2.7.1.11 PFK1 phosphofructokinase alpha subunit ATP + TAG6P -> ADP + TAG16P pfk1_2 YGR240C 2.7.1.11 PFK1 phosphofructokinase alpha subunit ATP + S7P -> ADP + S17P pfk1_3 YKL060C 4.1.2.13 FBA1 fructose-bisphosphate aldolase FDP <-> T3P2 + T3P1 fba1_1 YDR050C 5.3.1.1 TPI1 triosephosphate isomerase T3P2 <-> T3P1 tpi1 YJL052W 1.2.1.12 TDH1 Glyceraldehyde-3-phosphate dehydrogenase 1 T3P1 + PI + NAD <-> NADH + 13PDG tdh1 YJR009C 1.2.1.12 TDH2 glyceraldehyde 3-phosphate dehydrogenase T3P1 + PI + NAD <-> NADH + 13PDG tdh2 YGR192C 1.2.1.12 TDH3 Glyceraldehyde-3-phosphate dehydrogenase 3 T3P1 + PI + NAD <-> NADH + 13PDG tdh3 YCR012W 2.7.2.3 PGK1 phosphoglycerate kinase 13PDG + ADP <-> 3PG + ATP pgk1 YKL152C 5.4.2.1 GPM1 Phosphoglycerate mutase 13PDG <-> 23PDG gpm1_1 YKL152C 5.4.2.1 GPM1 Phosphoglycerate mutase 3PG <-> 2PG gpm1_2 YDL021W 5.4.2.1 GPM2 Similar to GPM1 (phosphoglycerate mutase) 3PG <-> 2PG gpm2 YOL056W 5.4.2.1 GPM3 phosphoglycerate mutase 3PG <-> 2PG gpm3 YGR254W 4.2.1.11 ENO1 enolase I 2PG <-> PEP eno1 YHR174W 4.2.1.11 ENO2 enolase 2PG <-> PEP eno2 YMR323W 4.2.1.11 ERR1 Protein with similarity to enolases 2PG <-> PEP eno3 YPL281C 4.2.1.11 ERR2 enolase related protein 2PG <-> PEP eno4 YOR393W 4.2.1.11 ERR1 enolase related protein 2PG <-> PEP eno5 YAL038W 2.7.1.40 CDC19 Pyruvate kinase PEP + ADP -> PYR + ATP cdc19 YOR347C 2.7.1.40 PYK2 Pyruvate kinase, glucose-repressed isoform PEP + ADP -> PYR + ATP pyk2 YER178w 1.2.4.1 PDA1 pyruvate dehydrogenase (lipoamide) alpha chain PYRm + COAm + pda1 precursor, E1 component, alpha unit NADm -> NADHm + CO2m + ACCOAm YBR221c 1.2.4.1 PDB1 pyruvate dehydrogenase (lipoamide) beta chain precursor, E1 component, beta unit YNL071w 2.3.1.12 LAT1 dihydrolipoamide S-acetyltransferase, E2 component Citrate cycle (TCA cycle) YNR001C 4.1.3.7 CIT1 Citrate synthase, Nuclear encoded mitochondrial ACCOAm + OAm -> COAm + CITm cit1 protein. YCR005C 4.1.3.7 CIT2 Citrate synthase, non-mitochondrial citrate synthase ACCOA + OA -> COA + CIT cit2 YPR001W 4.1.3.7 cit3 Citrate synthase, Mitochondrial isoform of citrate ACCOAm + OAm -> COAm + CITm cit3 synthase YLR304C 4.2.1.3 aco1 Aconitase, mitochondrial CITm <-> ICITm aco1 YJL200C 4.2.1.3 YJL200C aconitate hydratase homolog CITm <-> ICITm aco2 YNL037C 1.1.1.41 IDH1 Isocitrate dehydrogenase (NAD+) mito, subunit1 ICITm + NADm -> CO2m + idh1 NADHm + AKGm YOR136W 1.1.1.41 IDH2 Isocitrate dehydrogenase (NAD+) mito, subunit2 YDL066W 1.1.1.42 IDP1 Isocitrate dehydrogenase (NADP+) ICITm + NADPm -> idp1_1 NADPHm + OSUCm YLR174W 1.1.1.42 IDP2 Isocitrate dehydrogenase (NADP+) ICIT + NADP -> NADPH + OSUC idp2_1 YNL009W 1.1.1.42 IDP3 Isocitrate dehydrogenase (NADP+) ICIT + NADP -> NADPH + OSUC idp3_1 YDL066W 1.1.1.42 IDP1 Isocitrate dehydrogenase (NADP+) OSUCm -> CO2m + AKGm idp1_2 YLR174W 1.1.1.42 IDP2 Isocitrate dehydrogenase (NADP+) OSUC -> CO2 + AKG idp2_2 YNL009W 1.1.1.42 IDP3 Isocitrate dehydrogenase (NADP+) OSUC -> CO2 + AKG idp3_2 YIL125W 1.2.4.2 kgd1 alpha-ketoglutarate dehydrogenase complex, E1 AKGm + NADm + COAm -> kgd1a component CO2m + NADHm + SUCCOAm YDR148C 2.3.1.61 KGD2 Dihydrolipoamide S-succinyltransferase, E2 component YGR244C 6.2.1.4/ LSC2 Succinate-CoA ligase (GDP-forming) ATPm + SUCCm + COAm <-> lsc2 62.1.5 ADPm + PIm + SUCCOAm YOR142W 6.2.1.4/ LSC1 succinate-CoA ligase alpha subunit ATPm + ITCm + COAm <-> lsc1 6.2.1.5 ADPm + PIm + ITCCOAm Electron Transport System, Complex II YKL141w 1.3.5.1 SDH3 succinate dehydrogenase cytochrome b SUCCm + FADm <-> sdh3 FUMm + FADH2m YKL148c 1.3.5.1 SDH1 succinate dehydrogenase cytochrome b YLL041c 1.3.5.1 SDH2 Succinate dehydrogenase (ubiquinone) iron-sulfur protein subunit YDR178w 1.3.5.1 SDH4 succinate dehydrogenase membrane anchor subunit YLR164w 1.3.5.1 YLR164w strong similarity to SDH4P YMR118c 1.3.5.1 YMR118c strong similarity to succinate dehydrogenase YJL045w 1.3.5.1 YJL045w strong similarity to succinate dehydrogenase flavoprotein YEL047c 1.3.99.1 YEL047c soluble fumarate reductase, cytoplasmic FADH2m + FUM -> SUCC + FADm frds1 YJR051W 1.3.99.1 osm1 Mitochondrial soluble fumarate reductase involved in FADH2m + FUMm -> SUCCm + FADm osm1 osmotic regulation YPL262W 4.2.1.2 FUM1 Fumaratase FUMm <-> MALm fum1_1 YPL262W 4.2.1.2 FUM1 Fumaratase FUM <-> MAL fum1_2 YKL085W 1.1.1.37 MDH1 mitochondrial malate dehydrogenase MALm + NADm <-> NADHm + OAm mdh1 YDL078C 1.1.1.37 MDH3 MALATE DEHYDROGENASE, PEROXISOMAL MAL + NAD <-> NADH + OA mdh3 YOL126C 1.1.1.37 MDH2 malate dehydrogenase, cytoplasmic MAL + NAD <-> NADH + OA mdh2 Anaplerotic Reactions YER065C 4.1.3.1 ICL1 isocitrate lyase ICIT -> GLX + SUCC icl1 YPR006C 4.1.3.1 ICL2 Isocitrate lyase, may be nonfunctional ICIT -> GLX + SUCC icl2 YIR031C 4.1.3.2 dal7 Malate synthase ACCOA + GLX -> COA + MAL dal7 YNL117W 4.1.3.2 MLS1 Malate synthase ACCOA + GLX -> COA + MAL mls1 YKR097W 4.1.1.49 pck1 phosphoenolpyruvate carboxylkinase OA + ATP -> PEP + CO2 + ADP pck1 YLR377C 3.1.3.11 FBP1 fructose-1,6-bisphosphatase FDP -> F6P + PI fbp1 YGL062W 6.4.1.1 PYC1 pyruvate carboxylase PYR + ATP + CO2 -> ADP + OA + PI pyc1 YBR218C 6.4.1.1 PYC2 pyruvate carboxylase PYR + ATP + CO2 -> pyc2 ADP + OA + PI YKL029C 1.1.1.38 MAE1 mitochondrial malic enzyme MALm + NADPm -> mae1 CO2m + NADPHm + PYRm Pentose phosphate cycle YNL241C 1.1.1.49 zwf1 Glucose-6-phosphate-1-dehydrogenase G6P + NADP <-> zwf1 D6PGL + NADPH YNR034W 3.1.1.31 SOL1 Possible 6-phosphogluconolactonase D6PGL -> D6PGC sol1 YCR073W- 3.1.1.31 SOL2 Possible 6-phosphogluconolactonase D6PGL -> D6PGC sol2 A YHR163W 3.1.1.31 SOL3 Possible 6-phosphogluconolactonase D6PGL -> D6PGC sol3 YGR248W 3.1.1.31 SOL4 Possible 6-phosphogluconolactonase D6PGL -> D6PGC sol4 YGR256W 1.1.1.44 GND2 6-phophogluconate dehydrogenase D6PGC + NADP -> NADPH + gnd2 CO2 + RL5P YHR183W 1.1.1.44 GND1 6-phophogluconate dehydrogenase D6PGC + NADP -> NADPH + gnd1 CO2 + RL5P YJL121C 5.1.3.1 RPE1 ribulose-5-P 3-epimerase RL5P <-> X5P rpe1 YOR095C 5.3.1.6 RKI1 ribose-5-P isomerase RL5P <-> R5P rki1 YBR117C 2.2.1.1 TKL2 transketolase R5P + X5P <-> T3P1 + S7P tkl2_1 YBR117C 2.2.1.1 TKL2 transketolase X5P + E4P <-> F6P + T3P1 tkl2_2 YPR074C 2.2.1.1 TKL1 transketolase R5P + X5P <-> T3P1 + S7P tkl1_1 YPR074C 2.2.1.1 TKL1 transketolase X5P + E4P <-> F6P + T3P1 tkl1_2 YLR354C 2.2.1.2 TAL1 transaldolase T3PI + S7P <-> E4P + F6P tal1_1 YGR043C 2.2.1.2 YGR043C transaldolase T3PI + S7P <-> E4P + F6P tal1_2 YCR036W 2.7.1.15 RBK1 Ribokinase RIB + ATP -> R5P + ADP rbk1 _1 YCR036W 2.7.1.15 RBK1 Ribokinase DRIB + ATP -> DR5P + ADP rbk1_2 YKL127W 5.4.2.2 pgm1 phosphoglucomutase R1P <-> R5P pgm1_1 YKL127W 5.4.2.2 pgm1 phosphoglucomutase 1 G1P <-> G6P pgm1_2 YMR105C 5.4.2.2 pgm2 phosphoglucomutase R1P <-> R5P pgm2_1 YMR105C 5.4.2.2 pgm2 Phosphoglucomutase G1P <-> G6P pgm2_2 Mannose YER003C 5.3.1.8 PMI40 mannose-6-phosphate isomerase MAN6P <-> F6P pmi40 YFL045C 5.4.2.8 SEC53 phosphomannomutase MAN6P <-> MANIP sec53 YDL055C 2.7.7.13 PSA1 mannose-1-phosphate guanyltransferase, GTP + MANIP -> PPI + GDPMAN psa1 GDP-mannose pyrophosphorylase Fructose YIL107C 2.7.1.105 PFK26 6-Phosphofructose-2-kinase ATP + F6P -> ADP + F26P pfk26 YOL136C 2.7.1.105 pfk27 6-phosphofructo-2-kinase ATP + F6P -> ADP + F26P pfk27 YJL155C 3.1.3.46 FBP26 Fructose-2,6-biphosphatase F26P -> F6P + PI fbp26 — 2.7.1.56 — 1-Phosphofructokinase (Fructose 1-phosphate kinase) FIP + ATP -> FDP + ADP frc3 Sorbose S c does not metabolize sorbitol, erythritol, mannitol, xylitol, ribitol, arabinitol, galactinol YJR159W 1.1.1.14 SOR1 sorbitol dehydrogenase (L-iditol 2-dehydrogenase) SOT + NAD -> FRU + NADH sor1 Galactose metabolism YBR020W 2.7.1.6 gal1 galactokinase GLAC + ATP -> GAL1P + ADP gal1 YBR018C 2.7.7.10 gal7 galactose-1-phosphate uridyl transferase UTP + GAL1P <-> PPI + UDPGAL gal7 YBR019C 5.1.3.2 gal10 UDP-glucose 4-epimerase UDPGAL <-> UDPG gal10 YHL012W 2.7.7.9 YHL012W UTP--Glucose 1-Phosphate Uridylyltransferase G1P + UTP <-> UDPG + PPI ugp1_2 YKL035W 2.7.7.9 UGP1 Uridinephosphoglucose pyrophosphorylase G1P + UTP <-> UDPG + PPI ugp1_1 YBR184W 3.2.1.22 YBR184W Alpha-galactosidase (melibiase) MELI -> GLC + GLAC mel1_1 YBR184W 3.2.1.22 YBR184W Alpha-galactosidase (melibiase) DFUC -> GLC + GLAC mel1_2 YBR184W 3.2.1.22 YBR184W Alpha-galactosidase (melibiase) RAF -> GLAC + SUC mel1_3 YBR184W 3.2.1.22 YBR184W Alpha-galactosidase (melibiase) GLACL <-> MYOI + GLAC mel1_4 YBR184W 3.2.1.22 YBR184W Alpha-galactosidase (melibiase) EPM <-> MAN + GLAC mel1_5 YBR184W 3.2.1.22 YBR184W Alpha-galactosidase (melibiase) GGL <-> GL + GLAC mel1_6 YBR184W 3.2.1.22 YBR184W Alpha-galactosidase (melibiase) MELT <->SOT + GLAC mel1_7 YBR299W 3.2.1.20 MAL32 Maltase MLT -> 2 GLC mal32a YGR287C 3.2.1.20 YGR287C putative alpha glucosidase MLT -> 2 GLC mal32b YGR292W 3.2.1.20 MAL12 Maltase MLT -> 2 GLC mal12a YIL172C 3.2.1.20 YIL172C putative alpha glucosidase MLT -> 2 GLC mal12b YJL216C 3.2.1.20 YJL216C probable alpha-glucosidase (MALTase) MLT -> GLC mal12c YJL221C 3.2.1.20 FSP2 homology to maltase(alpha-D-glucosidase) MLT -> 2 GLC fsp2a YJL221C 3.2.1.20 FSP2 homology to maltase(alpha-D-glucosidase) 6DGLC -> GLAC + GLC fsp2b YBR018C 2.7.7.12 GAL7 UDPglucose--hexose-1-phosphate uridylyltransferase UDPG + GAL1P <-> G1P + UDPGAL unkrx10 Trehalose YBR126C 2.4.1.15 TPS1 trehalose-6-P synthetase, 56 kD synthase subunit of UDPG + G6P -> UDP + TRE6P tps1 trehalose-6-phosphate synthase\/phosphatase complex YML100W 2.4.1.15 tsl1 trehalose-6-P synthetase, 123 kD regulatory subunit of UDPG + G6P -> UDP + TRE6P tsl1 trehalose-6-phosphate synthase\/phosphatase complex\, homologous to TPS3 gene product YMR261C 2.4.1.15 TPS3 trehalose-6-P synthetase, 115 kD regulatory subunit of UDPG + G6P -> UDP + TRE6P tps3 trehalose-6-phosphate synthase\/phosphatase complex YDR074W 3.1.3.12 TPS2 Trehalose-6-phosphate phosphatase TRE6P -> TRE + PI tps2 YPR026W 3.2.1.28 ATH1 Acid trehalase TRE -> 2 GLC ath1 YBR001C 3.2.1.28 NTH2 Neutral trehalase, highly homologous to Nth1p TRE -> 2 GLC nth2 YDR001C 3.2.1.28 NTH1 neutral trehalase TRE -> 2 GLC nth1 Glycogen Metabolism (sucorose and sugar metabolism) YEL011W 2.4.1.18 glc3 Branching enzyme, 1,4-glucan-6-(1,4-glucano)- GLYCOGEN + PI -> G1P glc3 transferase YPR160W 2.4.1.1 GPH1 Glycogen phosphorylase GLYCOGEN + PI -> G1P gph1 YFR015C 2.4.1.11 GSY1 Glycogen synthase (UDP-gluocse--starch UDPG -> UDP + GLYCOGEN gsy1 glucosyltransferase) YLR258W 2.4.1.11 GSY2 Glycogen synthase (UDP-gluocse--starch UDPG -> UDP + GLYCOGEN gsy2 glucosyltransferase) Pyruvate Metabolism YAL054C 6.2.1.1 acs1 acetyl-coenzyme A synthetase ATPm + ACm + COAm -> acs1 AMPm + PPIm + ACCOAm YLR153C 6.2.1.1 ACS2 acetyl-coenzyme A synthetase ATP + AC + COA -> acs2 AMP + PPI + ACCOA YDL168W 1.2.1.1 SFA1 Formaldehyde dehydrogenase/long-chain alcohol FALD + RGT + NAD <-> FGT + NADH sfa1_1 dehydrogenase YJL068C 3.1.2.12 — S-Formylglutathione hydrolase FGT <-> RGT + FOR unkrx11 YGR087C 4.1.1.1 PDC6 pyruvate decarboxylase PYR -> CO2 + ACAL pdc6 YLR134W 4.1.1.1 PDC5 pyruvate decarboxylase PYR -> CO2 + ACAL pdc5 YLR044C 4.1.1.1 pdc1 pyruvate decarboxylase PYR -> CO2 + ACAL pdc1 YBL015W 3.1.2.1 ACH1 acetyl CoA hydrolase COA + AC -> ACCOA ach1_1 YBL015W 3.1.2.1 ACH1 acetyl CoA hydrolase COAm + ACm -> ACCOAm ach1_2 YDL131W 4.1.3.21 LYS21 probable homocitrate synthase, mitochondrial isozyme ACCOA + AKG -> HCIT + COA lys21 precursor YDL182W 4.1.3.21 LYS20 homocitrate synthase, cytosolic isozyme ACCOA + AKG -> HCIT + COA lys20 YDL182W 4.1.3.21 LYS20 Homocitrate synthase ACCOAm + AKGm -> HCITm + COAm lys20a YGL256W 1.1.1.1 adh4 alcohol dehydrogenase isoenzyme IV ETH + NAD <-> ACAL + NADH adh4 YMR083W 1.1.1.1 adh3 alcohol dehydrogenase isoenzyme III ETHm + NADm <-> ACALm + NADHm adh3 YMR303C 1.1.1.1 adh2 alcohol dehydrogenase II ETH + NAD <-> ACAL + NADH adh2 YBR145W 1.1.1.1 ADH5 alcohol dehydrogenase isoenzyme V ETH + NAD <-> ACAL + NADH adh5 YOL086C 1.1.1.1 adh1 Alcohol dehydrogenase I ETH + NAD <-> ACAL + NADH adh1 YDL168W 1.1.1.1 SFA1 Alcohol dehydrogenase I ETH + NAD <-> ACAL + NADH sfa1_2 Glyoxylate and dicarboxylate metabolism Glyoxal Pathway YML004C 4.4.1.5 GLO1 Lactoylglutathione lyase, glyoxalase I RGT + MTHGXL <-> LGT glo1 YDR272W 3.1.2.6 GLO2 Hydroxyacylglutathione hydrolase LGT -> RGT + LAC glo2 YOR040W 3.1.2.6 GLO4 glyoxalase II (hydroxyacylglutathione hydrolase) LGTm -> RGTm + LACm glo4 Energy Metabolism Oxidative Phosphorylation YBR011C 3.6.1.1 ipp1 Inorganic pyrophosphatase PPI -> 2 PI ipp1 YMR267W 3.6.1.1 ppa2 mitochondrial inorganic pyrophosphatase PPIm -> 2 PIm ppa2 1.2.2.1 FDNG Formate dehydrogenase FOR + Qm -> QH2m + CO2 + 2 HEXT fdng YML120C 1.6.5.3 NDI1 NADH dehydrogenase (ubiquinone) NADHm + Qm -> QH2m + NADm ndi1 YDL085W 1.6.5.3 NDH2 Mitochondrial NADH dehydrogenase that catalyzes the NADH + Qm -> QH2m + NAD ndh2 oxidation of cytosolic NADH YMR145C 1.6.5.3 NDH1 Mitochondrial NADH dehydrogenase that catalyzes the NADH + Qm -> QH2m + NAD ndh1 oxidation of cytosolic NADH YHR042W 1.6.2.4 NCP1 NADPH--ferrihemoprotein reductase NADPH + 2 FERIm -> ncp1 NADP + 2 FEROm YKL141w 1.3.5.1 SDH3 succinate dehydrogenase cytochrome b FADH2m + Qm <-> FADm + QH2m fad YKL148c 1.3.5.1 SDH1 succinate dehydrogenase cytochrome b YLL041c 1.3.5.1 SDH2 succinate dehydrogenase cytochrome b YDR178w 1.3.5.1 SDH4 succinate dehydrogenase cytochrome b Electron Transport System, Complex III YEL024W 1.10.2.2 RIP1 ubiquinol-cytochrome c reductase iron-sulfur subunit O2m + 4 FEROm + 6 Hm -> 4 FERIm cyto Q0105 1.10.2.2 CYTB ubiquinol-cytochrome c reductase cytochrome b subunit YOR065W 1.10.2.2 CYT1 ubiquinol-cytochrome c reductase cytochrome c1 subunit YBL045C 1.10.2.2 COR1 ubiquinol-cytochrome c reductase core subunit 1 YPR191W 1.10.2.2 QCR1 ubiquinol-cytochrome c reductase core subunit 2 YPR191W 1.10.2.2 QCR2 ubiquinol-cytochrome c reductase YFR033C 1.10.2.2 QCR6 ubiquinol-cytochrome c reductase subunit 6 YDR529C 1.10.2.2 QCR7 ubiquinol-cytochrome c reductase subunit 7 YJL166W 1.10.2.2 QCR8 ubiquinol-cytochrome c reductase subunit 8 YGR183C 1.10.2.2 QCR9 ubiquinol-cytochrome c reductase subunit 9 YHR001W- 1.10.2.2 QCR10 ubiquinol-cytochrome c reductase subunit 10 A Electron Transport System, Complex IV Q0045 1.9.3.1 COX1 cytochrome c oxidase subunit I QH2m + 2 FERIm + 15 cytr Hm -> Qm + 2 FEROm Q0250 1.9.3.1 COX2 cytochrome c oxidase subunit I Q0275 1.9.3.1 COX3 cytochrome c oxidase subunit I YDL067C 1.9.3.1 COX9 cytochrome c oxidase subunit I YGL187C 1.9.3.1 COX4 cytochrome c oxidase subunit I YGL191W 1.9.3.1 COX13 cytochrome c oxidase subunit I YHR051W 1.9.3.1 COX6 cytochrome c oxidase subunit I YIL111W 1.9.3.1 COX5B cytochrome c oxidase subunit I YLR038C 1.9.3.1 COX12 cytochrome c oxidase subunit I YLR395C 1.9.3.1 COX8 cytochrome c oxidase subunit I YMR256C 1.9.3.1 COX7 cytochrome c oxidase subunit I YNL052W 1.9.3.1 COX5A cytochrome c oxidase subunit I ATP Synthase YBL099W 3.6.1.34 ATP1 F1F0-ATPase complex, F1 alpha subunit ADPm + PIm -> ATPm + 3 Hm atp1 YPL271W 3.6.1.34 ATP15 F1F0-ATPase complex, F1 epsilon subunit YDL004W 3.6.1.34 ATP16 F-type H+-transporting ATPase delta chain Q0085 3.6.1.34 ATP6 F1F0-ATPase complex, FO A subunit YBR039W 3.6.1.34 ATP3 F1F0-ATPase complex, F1 gamma subunit YBR127C 3.6.1.34 VMA2 H+-ATPase V1 domain 60 KD subunit, vacuolar YPL078C 3.6.1.34 ATP4 F1F0-ATPase complex, F1 delta subunit YDR298C 3.6.1.34 ATP5 F1F0-ATPase complex, OSCP subunit YDR377W 3.6.1.34 ATP17 ATP synthase complex, subunit f YJR121W 3.6.1.34 ATP2 F1F0-ATPase complex, F1 beta subunit YKL016C 3.6.1.34 ATP7 F1F0-ATPase complex, FO D subunit YLR295C 3.6.1.34 ATP14 ATP synthase subunit h Q0080 3.6.1.34 ATP8 F-type H+-transporting ATPase subunit 8 Q0130 3.6.1.34 ATP9 F-type H+-transporting ATPase subunit c YOL077W- 3.6.1.34 ATP19 ATP synthase k chain, mitochondrial A YPR020W 3.6.1.34 ATP20 subunit G of the dimeric form of mitochondrial F1F0- ATP synthase YLR447C 3.6.1.34 VMA6 V-type H+-transporting ATPase subunit AC39 YGR020C 3.6.1.34 VMA7 V-type H+-transporting ATPase subunit F YKL080W 3.6.1.34 VMA5 V-type H+-transporting ATPase subunit C YDL185W 3.6.1.34 TFP1 V-type H+-transporting ATPase subunit A YBR127C 3.6.1.34 VMA2 V-type H+-transporting ATPase subunit B YOR332W 3.6.1.34 VMA4 V-type H+-transporting ATPase subunit E YEL027W 3.6.1.34 CUP5 V-type H+-transporting ATPase proteolipid subunit YHR026W 3.6.1.34 PPA1 V-type H+-transporting ATPase proteolipid subunit YPL234C 3.6.1.34 TFP3 V-type H+-transporting ATPase proteolipid subunit YMR054W 3.6.1.34 STV1 V-type H+-transporting ATPase subunit I YOR270C 3.6.1.34 VPH1 V-type H+-transporting ATPase subunit I YEL051W 3.6.1.34 VMA8 V-type H+-transporting ATPase subunit D YHR039C-A 3.6.1.34 VMA10 vacuolar ATP synthase subunit G YPR036W 3.6.1.34 VMA13 V-type H+-transporting ATPase 54 kD subunit Electron Transport System, Complex IV Q0045 1.9.3.1 COX1 cytochrome-c oxidase subunit I 4 FEROm + O2m + 6 Hm -> 4 FERIm cox1 Q0275 1.9.3.1 COX3 Cytochrome-c oxidase subunit III, mitochondrially- coded Q0250 1.9.3.1 COX2 cytochrome-c oxidase subunit II YDL067C 1.9.3.1 COX9 Cytochrome-c oxidase YGL187C 1.9.3.1 COX4 cytochrome-c oxidase chain IV YGL191W 1.9.3.1 COX13 cytochrome-c oxidase chain VIa YHR051W 1.9.3.1 COX6 cytochrome-c oxidase subunit VI YIL111W 1.9.3.1 COX5b cytochrome-c oxidase chain Vb YLR038C 1.9.3.1 COX12 cytochrome-c oxidase, subunit VIB YLR395C 1.9.3.1 COX8 cytochrome-c oxidase chain VIII YMR256C 1.9.3.1 COX7 cytochrome-c oxidase, subunit VII YNL052W 1.9.3.1 COX5A cytochrome-c oxidase chain V.A precursor YML054C 1.1.2.3 cyb2 Lactic acid dehydrogenase 2 FERIm + LLACm -> cyb2 PYRm + 2 FEROm YDL174C 1.1.2.4 DLD1 mitochondrial enzyme D-lactate ferricytochrome c 2 FERIm + LACm -> PYRm + 2 FEROm dld1 oxidoreductase Methane metabolism YPL275W 1.2.1.2 YPL275W putative formate dehydrogenase/putative pseudogene FOR + NAD -> CO2 + NADH tfo1a YPL276W 1.2.1.2 YPL276W putative formate dehydrogenase/putative pseudogene FOR + NAD -> CO2 + NADH tfo1b YOR388C 1.2.1.2 FDH1 Protein with similarity to formate dehydrogenases FOR + NAD -> CO2 + NADH fdh1 Nitrogen metabolism YBR208C 6.3.4.6 DUR1 urea amidolyase containing urea carboxylase/ ATP + UREA + CO2 <-> dur1 allophanate hydrolase ADP + PI + UREAC YBR208C 3.5.1.54 DUR1 Allophanate hydrolase UREAC -> 2 NH3 + 2 CO2 dur2 YJL126W 3.5.5.1 NIT2 nitrilase ACNL -> INAC + NH3 nit2 Sulfur metabolism (Cystein biosynthesis maybe) YJR137C 1.8.7.1 ECM17 Sulfite reductase H2SO3 + 3 NADPH <-> H2S + 3 NADP ecm17 Lipid Metabolism Fatty acid biosynthesis YER015W 6.2.1.3 FAA2 Long-chain-fatty-acid--CoA ligase, Acyl-CoA ATP + LCCA + COA <-> faa2 synthetase AMP + PPI + ACOA YIL009W 6.2.1.3 FAA3 Long-chain-fatty-acid--CoA ligase, Acyl-CoA ATP + LCCA + COA <-> faa3 synthetase AMP + PPI + ACOA YOR317W 6.2.1.3 FAA1 Long-chain-fatty-acid--CoA ligase, Acyl-CoA ATP + LCCA + COA <-> faa1 synthetase AMP + PPI + ACOA YMR246W 6.2.1.3 FAA4 Acyl-CoA synthase (long-chain fatty acid CoA ligase); ATP + LCCA + COA <-> faa4 contributes to activation of imported myristate AMP + PPI + ACOA YKR009C 1.1.1.- FOX2 3-Hydroxyacyl-CoA dehydrogenase HACOA + NAD <-> OACOA + NADH fox2b YIL160C 2.3.1.16 pot1 3-Ketoacyl-CoA thiolase OACOA + COA -> ACOA + ACCOA pot1_1 YPL028W 2.3.1.9 erg10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2 ACCOA <-> COA + AACCOA erg10_1 COA THIOLASE YPL028W 2.3.1.9 erg10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2 ACCOAm <-> COAm + AACCOAm erg10_2 COA THIOLASE (mitoch) Fatty Acids Metabolism Mitochondrial type II fatty acid synthase YKL192C 1.6.5.3 ACP1 Acyl carrier protein, component of NADHm + Qm -> NADm + QH2m ACP1 mitochondrial type II fatty acid synthase YER061C — CEM1 Beta-ketoacyl-ACP synthase, mitochondrial (3-oxoacyl-[Acyl-carrier-protein] synthase) YOR221C — MCT1 Malonyl CoA-acyl carrier protein transferase YKL055C — OAR1 3-Oxoacyl-[acyl-carrier-protein] reductase YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 4 MALACPm + TypeII_1 YER061C/ -/- CEM1/ 8 NADPHm -> 8 YOR221C/ MCT1/ NADPm + C100ACPm + YKL055C OAR1 4 CO2m + 4 ACPm YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 5 MALACPm + 10 TypeII_2 YER061C/ -/- CEM1/ NADPHm -> 10 YOR221C/ MCT1/ NADPm + C120ACPm + 5 YKL055C OAR1 CO2m + 5 ACPm YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 6 MALACPm + 12 TypeII_3 YER061C/ -/- CEM1/ NADPHm -> 12 YOR221C/ MCT1/ NADPm + C140ACPm + 6 YKL055C OAR1 CO2m + 6 ACPm YKL192C/ 1.6.5.3/- ACP1/ Type II fatty acid synthase ACACPm + 6 MALACPm + 11 TypeII_4 YER061C/ -/- CEM1/ NADPHm -> 11 YOR221C/ MCT1/ NADPm + C141ACPm + 6 YKL055C OAR1 CO2m + 6 ACPm YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 7 MALACPm + 14 TypeII_5 YER061C/ -/- CEM1/ NADPHm -> 14 YOR221C/ MCT1/ NADPm + C160ACPm + 7 YKL055C OAR1 CO2m + 7 ACPm YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 7 MALACPm + 13 TypeII_6 YER061C/ -/- CEM1/ NADPHm -> 13 YOR221C/ MCT1/ NADPm + C161ACPm + 7 YKL055C OAR1 CO2m + 7 ACPm YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 8 MALACPm + 16 TypeII_7 YER061C/ -/- CEM1/ NADPHm -> 16 YOR221C/ MCT1/ NADPm + C180ACPm + 8 YKL055C OAR1 CO2m + 8 ACPm YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 8 MALACPm + 15 TypeII_8 YER061C/ -/- CEM1/ NADPHm -> 15 YOR221C/ MCT1/ NADPm + C181ACPm + 8 YKL055C OAR1 CO2m + 8 ACPm YKL192C/ 1.6.5.3/-/ ACP1/ Type II fatty acid synthase ACACPm + 8 MALACPm + 14 TypeII_9 YER061C/ -/- CEM1/ NADPHm -> 14 YOR221C/ MCT1/ NADPm + C182ACPm + 8 YKL055C OAR1 CO2m + 8 ACPm Cytosolic fatty acid synthesis YNR016C 6.4.1.2 ACC1 acetyl-CoA carboxylase (ACC)/biotin carboxylase ACCOA + ATP + CO2 <-> acc1 6.3.4.14 MALCOA + ADP + PI YKL182w 4.2.1.61, fas1 fatty-acyl-CoA synthase, beta chain MALCOA + ACP <-> MALACP + COA fas1_1 1.3.1.9, 2.3.1.38, 2.3.1.39, 3.1.2.14, 2.3.1.86 YPL231w 2.3.1.85, FAS2 fatty-acyl-CoA synthase, alpha chain 1.1.1.100, 2.3.1.41 YKL182w 4.2.1.61, fas1 fatty-acyl-CoA synthase, beta chain ACCOA + ACP <-> ACACP + COA fas1_2 1.3.1.9, 2.3.1.38; 2.3.1.39, 3.1.2.14, 2.3.1.86 YER061C 2.3.1.41 CEM1 3-Oxoacyl-[acyl-carrier-protein] synthase MALACPm + ACACPm -> cem1 ACPm + CO2m + 3OACPm YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase (C10, 0), fatty acyl CoA ACACP + 4 MALACP + 8 c100sn YNR016C/ 6.3.4.1, ACC1/ NADPH -> 8 NADP + YKL182W/ 4.2.3.1.85; fas1/ synthase C100ACP + 4 CO2 + 4 ACP YPL231w 1.1.1.100; FAS2/ 2.3.1.41; 4.2.1.61 YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase (C12, 0), fatty acyl CoA ACACP + 5 MALACP + 10 c120sn YNR016C/ 6.3.4.1, ACC1/ NADPH -> 10 NADP + YKL182W/ 4.2.3.1.85, fas1/ synthase C120ACP + 5 CO2 + 5 ACP YPL231w 1.1.1.100, FAS2/ 2.3.1.41, 4.2.1.61 YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase (C14, 0) ACACP + 6 MALACP + 12 c140sn YNR016C/ 6.3.4.1; ACC1/ NADPH -> 12 NADP + YKL182W/ 4.2.3.1.85; fas1/ C140ACP + 6 CO2 + 6 ACP YPL231w 1.1.1.100, FAS2/ 2.3.1.41; 4.2.1.61 YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase I (C14, 1) ACACP + 6 MALACP + 11 c141sy YNR016C/ 6.3.4.1, ACC1/ NADPH -> 11 NADP + YKL182W/ 4.2.3.1.85, fas1/ C141ACP + 6 CO2 + 6 ACP YPL231w 1.1.1.100; FAS2/ 2.3.1.41; 4.2.1.61 YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase I (C16, 0) ACACP + 7 MALACP + 14 c160sn YNR016C/ 6.3.4.1, ACC1/ NADPH -> 14 NADP + YKL182W/ 4.2.3.1.85; fas1/ C160ACP + 7 CO2 + 7 ACP YPL231w 1.1.1.100, FAS2/ 2.3.1.41, 4.2.1.61 YGR037C/ 6.4.1.2; ACB1/ b-Ketoacyl-ACP synthase I (C16, 1) ACACP + 7 MALACP + 13 c161sy YNR016C/ 6.3.4.1; ACC1/ NADPH -> 13 NADP + YKL182W/ 4.2.3.1.85; fas1/ C161ACP + 7 CO2 + 7 ACP YPL231w 1.1.1.100, FAS2/ 2.3.1.41, 4.2.1.61 YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase I (C18, 0) ACACP + 8 MALACP + 16 c180sy YNR016C/ 6.3.4.1; ACC1/ NADPH -> 16 NADP + YKL182W/ 42.3.1.85; fas1/ C180ACP + 8 CO2 + 8 ACP YPL231w 1.1.1.100, FAS2/ 2.3.1.41, 4.2.1.61 YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase I (C18, 1) ACACP + 8 MALACP + 15 c181sy YNR016C/ 6.3.4.1; ACC1/ NADPH -> 15 NADP + YKL182W/ 4.2.3.1.85, fas1/ C181ACP + 8 CO2 + 8 ACP YPL231w 1.1.1.100, FAS2/ 2.3.1.41; 4.2.1.61 YGR037C/ 6.4.1.2, ACB1/ b-Ketoacyl-ACP synthase I (C18, 2) ACACP + 8 MALACP + 14 c182sy YNR016C/ 6.3.4.1, ACC1/ NADPH -> 14 NADP + YKL182W/ 4.2.3.1.85; fas1/ C182ACP + 8 CO2 + 8 ACP YPL231w 1.1.1.100, FAS2/ 2.3.1.41, 4.2.1.61 YKL182W 4.2.1.61 fas1 3-hydroxypalmitoyl-[acyl-carrier protein] dehydratase 3HPACP <-> 2HDACP fas1_3 YKL182W 1.3.1.9 fas1 Enoyl-ACP reductase AACP + NAD <- > 23DAACP + NADH fas1_4 Fatty acid degradation YGL205W/ 1.3.3.6/ POX1/ Fatty acid degradation C140 + ATP + 7 COA + 7 FADm + 7 c140dg YKR009C/ 2.3.1.18 FOX2/ NAD -> AMP + YIL160C POT3 PPI + 7 FADH2m + 7 NADH + 7 ACCOA YGL205W/ 1.3.3.6/ POX1/ Fatty acid degradation C160 + ATP + 8 COA + 8 FADm + 8 c160dg YKR009C/ 2.3.1.18 FOX2/ NAD -> AMP + YIL160C POT3 PPI + 8 FADH2m + 8 NADH + 8 ACCOA YGL205W/ 1.3.3.6/ POX1/ Fatty acid degradation C180 + ATP + 9 COA + 9 c180dg YKR009C/ 2.3.1.18 FOX2/ FADm + 9 NAD -> AMP + YIL160C POT3 PPI + 9 FADH2m + 9 NADH + 9 ACCOA Phospholipid Biosynthesis — — Glycerol-3-phosphate acyltransferase GL3P + 0.017 C100ACP + 0.062 Gat1_1 C120ACP + 0.1 C140ACP + 0.27 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> AGL3P + ACP — — Glycerol-3-phosphate acyltransferase GL3P + 0.017 C100ACP + 0.062 Gat2 _1 C120ACP + 0.1 C140ACP + 0.27 + C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> AGL3P + ACP — — Glycerol-3-phosphate acyltransferase T3P2 + 0.017 C100ACP + 0.062 Gat1_2 C120ACP + 0.1 C140ACP + 0.27 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> AT3P2 + ACP — — Glycerol-3-phosphate acyltransferase T3P2 + 0.017 C100ACP + 0.062 Gat2_2 C120ACP + 0.1 C140ACP + 0.27 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> AT3P2 + ACP — — Acyldihydroxyacetonephosphate reductase AT3P2 + NADPH -> AGL3P + NADP ADHAPR YDL052C 2.3.1.51 SLC1 1-Acylglycerol-3-phosphate acyltransferase AGL3P + 0.017 C100ACP + 0.062 slc1 C120ACP + 0.100 C140ACP + 0.270 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> PA + ACP — 2.3.1.51 — 1-Acylglycerol-3-phosphate acyltransferase AGL3P + 0.017 C100ACP + 0.062 AGAT C120ACP + 0.100 C140ACP + 0.270 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> PA + ACP YBR029C 2.7.7.41 CDS1 CDP-Diacylglycerol synthetase PAm + CTPm <-> CDPDGm + PPIm cds1a YBR029C 2.7.7.41 CDS1 CDP-Diacylglycerol synthetase PA + CTP <-> CDPDG + PPI cds1b YER026C 2.7.8.8 cho1 phosphatidylserine synthase CDPDG + SER <-> CMP + PS cho1a YER026C 2.7.8.8 cho1 Phosphatidylserine synthase CDPDGm + SERm <-> CMPm + PSm cho1b YGR170W 4.1.1.65 PSD2 phosphatidylserine decarboxylase located in vacuole or PS -> PE + CO2 psd2 Golgi YNL169C 4.1.1.65 PSD1 Phosphatidylserine Decarboxylase 1 PSm -> PEm + CO2m psd1 YGR157W 2.1.1.17 CHO2 Phosphatidylethanolamine N-methyltransferase SAM + PE -> SAH + PMME cho2 YJR073C 2.1.1.16 OPI3 Methylene-fatty-acyl-phospholipid synthase. SAM + PMME -> SAH + PDME opi3_1 YJR073C 2.1.1.16 OPI3 Phosphatidyl-N-methylethanolamine N- PDME + SAM -> PC + SAH opi3_2 methyltransferase YLR133W 2.7.1.32 CKI1 Choline kinase ATP + CHO -> ADP + PCHO cki1 YGR202C 2.7.7.15 PCT1 Cholinephosphate cytidylyltransferase PCHO + CTP -> CDPCHO + PPI pct1 YNL130C 2.7.8.2 CPT1 Diacylglycerol cholinephosphotransferase CDPCHO + DAGLY -> PC + CMP cpt1 YDR147W 2.7.1.82 EKI1 Ethanolamine kinase ATP + ETHM -> ADP + PETHM eki1 YGR007W 2.7.7.14 MUQ1 Phosphoethanolamine cytidylyltransferase PETHM + CTP -> CDPETN + PPI ect1 YHR123W 2.7.8.1 EPT1 Ethanolaminephosphotransferase. CDPETN + DAGLY <-> CMP + PE ept1 YJL153C 5.5.1.4 ino1 myo-Inositol-1-phosphate synthase G6P -> MI1P ino1 YHR046C 3.1.3.25 INM1 myo-Inositol-1(or 4)-monophosphatase MI1P -> MYOI + PI impal YPR113W 2.7.8.11 PIS1 phosphatidylinositol synthase CDPDG + MYOI -> CMP + PINS pis1 YJR066W 2.7.1.137 tor1 1-Phosphatidylinositol 3-kinase ATP + PINS -> ADP + PINSP tor1 YKL203C 2.7.1.137 tor2 1-Phosphatidylinositol 3-kinase ATP + PINS -> ADP + PINSP tor2 YLR240W 2.7.1.137 vps34 1-Phosphatidylinositol 3-kinase ATP + PINS -> ADP + PINSP vps34 YNL267W 2.7.1.67 PIK1 Phosphatidylinositol 4-kinase (PI 4-kinase), generates ATP + PINS -> ADP + PINS4P pik1 PtdIns 4-P YLR305C 2.7.1.67 STT4 Phosphatidylinositol 4-kinase ATP + PINS -> ADP + PINS4P sst4 YFR019W 2.7.1.68 FAB1 PROBABLE PHOSPHATIDYLINOSITOL-4- PINS4P + ATP -> D45PI + ADP fab1 PHOSPHATE 5-KINASE, 1-phosphatidylinositol-4- phosphate kinase YDR208W 2.7.1.68 MSS4 Phosphatidylinositol-4-phosphate PINS4P + ATP -> D45PI + ADP mss4 5-kinase, required for proper organization of the actin cytoskeleton YPL268W 3.1.4.11 plc1 1-phosphatidylinositol-4,5-bisphosphate D45PI -> TPI + DAGLY plc1 phosphodiesterase YCL004W 2.7.8.8 PGS1 CDP-diacylglycerol - serine CDPDGm + GL3Pm <-> CMPm + PGPm pgs1 O-phosphatidyltransferase — 3.1.3.27 Phosphatidylglycerol phosphate phosphatase A PGPm -> PIm + PGm Pgpa YDL142C 2.7.8.5 CRD1 Cardiolipin synthase CDPDGm + PGm -> CMPm + CLm crd1 YDR284C DPP1 diacylglycerol pyrophosphate phosphatase PA -> DAGLY + PI dpp1 YDR503C LPP1 lipid phosphate phosphatase DGPP -> PA + PI lpp1 Sphingoglycolipid Metabolism YDR062W 2.3.1.50 LCB2 Serine C-palmitoyltransferase PALCOA + SER -> COA + lcb2 DHSPH + CO2 YMR296C 2.3.1.50 LCB1 Serine C-palmitoyltransferase PALCOA + SER -> lcb1 COA + DHSPH + CO2 YBR265w 1.1.1.102 TSC10 3-Dehydrosphinganine reductase DHSPH + NADPH -> SPH + NADP tsc10 YDR297W SUR2 SYRINGOMYCIN RESPONSE PROTEIN 2 SPH + O2 + NADPH -> PSPH + NADP sur2 — Ceramide synthase PSPH + C260COA -> CER2 + COA csyna — Ceramide synthase PSPH + C240COA -> csynb CER2 + COA YMR272C SCS7 Ceramide hydroxylase that CER2 + NADPH + O2 -> CER3 + NADP scs7 hydroxylates the C-26 fatty- acyl moiety of mositol-phosphorylceramide YKL004W AUR1 IPS synthase, AUREOBASIDIN A RESISTANCE CER3 + PINS -> IPC aur1 PROTEIN YBR036C CSG2 Protein required for synthesis of the mannosylated IPC + GDPMAN -> MIPC csg2 sphingolipids YPL057C SUR1 Protein required for synthesis of the mannosylated IPC + GDPMAN -> MIPC sur1 sphingolipids YDR072C 2.-.-.- IPT1 MIP2C synthase, MANNOSYL MIPC + PINS -> MIP2C ipt1 DIPHOSPHORYLINOSITOL CERAMIDE SYNTHASE YOR171C LCB4 Long chain base kinase, involved in sphingolipid SPH + ATP -> DHSP + ADP lcb4_1 metabolism YLR260W LCB5 Long chain base kinase, involved in sphingolipid SPH + ATP -> DHSP + ADP lcb5_1 metabolism YOR171C LCB4 Long chain base kinase, involved in sphingolipid PSPH + ATP -> PHSP + ADP lcb4_2 metabolism YLR260W LCB5 Long chain base kinase, involved in sphingolipid PSPH + ATP -> PHSP + ADP lcb5_2 metabolism YJL134W LCB3 Sphingoid base-phosphate phosphatase, putative DHSP -> SPH + PI lcb3 regulator of sphingolipid metabolism and stress response YKR053C YSR3 Sphingoid base-phosphate phosphatase, putative DHSP -> SPH + PI ysr3 regulator of sphingolipid metabolism and stress response YDR294C DPL1 Dihydrosphingosme-1-phosphate lyase DHSP -> PETHM + C16A dpl1 Sterol biosynthesis YML126C 4.1.3.5 HMGS 3-hydroxy-3-methylglutaryl coenzyme A synthase H3MCOA + COA <-> hmgs ACCOA + AACCOA YLR450W 1.1.1.34 hmg2 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) MVL + COA + 2 NADP <-> hmg2 reductase isozyme H3MCOA + 2 NADPH YML075C 1.1.1.34 hmg1 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) MVL + COA + 2 NADP <-> hmg1 reductase isozyme H3MCOA + 2 NADPH YMR208W 2.7.1.36 erg12 mevalonate kinase ATP + MVL -> ADP + PMVL erg12_1 YMR208W 2.7.1.36 erg12 mevalonate kinase CTP + MVL -> CDP + PMVL erg12_2 YMR208W 2.7.1.36 erg12 mevalonate kinase GTP + MVL -> GDP + PMVL erg12_3 YMR208W 2.7.1.36 erg12 mevalonate kinase UTP + MVL -> UDP + PMVL erg12_4 YMR220W 2.7.4.2 ERG8 48 kDa Phosphomevalonate kinase ATP + PMVL -> ADP + PPMVL erg8 YNR043W 4.1.1.33 MVD1 Diphosphomevalonate decarboxylase ATP + PPMVL -> ADP + mvd1 PI + IPPP + CO2 YPL117C 5.3.3.2 idi1 Isopentenyl diphosphate.dimethylallyl diphosphate IPPP <-> DMPP idi1 isomerase (IPP isomerase) YJL167W 2.5.1.1 ERG20 prenyltransferase DMPP + IPPP -> GPP + PPI erg20_1 YJL167W 2.5.1.10 ERG20 Farnesyl diphosphate synthetase (FPP synthetase) GPP + IPPP -> FPP + PPI erg20_2 YHR190W 2.5.1.21 ERG9 Squalene synthase 2 FPP + NADPH -> NADP + SQL erg9 YGR175C 1.14.99.7 ERG1 Squalene monoxygenase SQL + O2 + NADP -> S23E + NADPH erg1 YHR072W 5.4.99.7 ERG7 2,3-oxidosqualene-lanosterol cyclase S23E -> LNST erg7 YHR007c 1.14.14.1 erg11 cytochrome P450 lanosterol 14a-demethylase LNST + RFP + O2 -> IGST + OFP erg11_1 YNL280c 1.-.-.- ERG24 C-14 sterol reductase IGST + NADPH -> DMZYMST + NADP erg24 YGR060w 1.-.-.- ERG25 C-4 sterol methyl oxidase 3 O2 + DMZYMST -> IMZYMST erg25_1 YGL001c 5.3.3.1 ERG26 C-3 sterol dehydrogenase (C-4 decarboxylase) IMZYMST -> IIMZYMST + CO2 erg26_1 YLR100C YLR100C C-3 sterol keto reductase IIMZYMST + NADPH -> MZYMST + erg11_2 NADP YGR060w 1.-.-.- ERG25 C-4 sterol methyl oxidase 3 O2 + MZYMST -> IZYMST erg25_2 YGL001c 5.3.3.1 ERG26 C-3 sterol dehydrogenase (C-4 decarboxylase) IZYMST -> IIZYMST + CO2 erg26_2 YLR100C YLR100C C-3 sterol keto reductase IIZYMST + NADPH -> ZYMST + erg11_3 NADP YML008c 2.1.1.41 erg6 S-adenosyl-methionine delta-24-sterol-c- ZYMST + SAM -> FEST + SAH erg6 methyltransferase YMR202W ERG2 C-8 sterol isomerase FEST -> EPST erg2 YLR056w 1.-.-.- ERG3 C-5 sterol desaturase EPST + O2 + NADPH -> NADP + erg3 ERTROL YMR015c 1.14.14.- ERG5 C-22 sterol desaturase ERTROL + O2 + NADPH -> NADP + erg5 ERTROL YGL012w 1.-.-.- ERG4 sterol C-24 reductase ERTEOL + NADPH -> ERGOST + erg4 NADP LNST + 3 O2 + 4 unkrxn3 NADPH + NAD -> MZYMST + CO2 + 4 NADP + NADH MZYMST + 3 O2 + 4 unkrxn4 NADPH + NAD -> ZYMST + CO2 + 4 NADP + NADH 5.3.3.5 Cholestenol delta-isomerase ZYMST + SAM -> ERGOST + SAH cdisoa Nucleotide Metabolism Histidine Biosynthesis YOL061W 2.7.6.1 PRS5 ribose-phosphate pyrophosphokinase R5P + ATP <-> PRPP + AMP prs5 YBL068W 2.7.6.1 PRS4 ribose-phosphate pyrophosphokinase 4 R5P + ATP <-> PRPP + AMP prs4 YER099C 2.7.6.1 PRS2 ribose-phosphate pyrophosphokinase 2 R5P + ATP <-> PRPP + AMP prs2 YHL011C 2.7.6.1 PRS3 ribose-phosphate pyrophosphokinase 3 R5P + ATP <-> PRPP + AMP prs3 YKL181W 2.7.6.1 PRS1 ribose-phosphate pyrophosphokinase R5P + ATP <-> PRPP + AMP prs1 YIR027C 3.5.2.5 dal1 allantomase ATN <-> ATT dal1 YIR029W 3.5.3.4 dal2 allantoicase ATT <-> UGC + UREA dal2 YIR032C 3.5.3.19 dal3 ureidoglycolate hydrolase UGC <-> GLX + 2 NH3 + CO2 dal3 Purine metabolism YJL005W 4.6.1.1 CYR1 adenylate cyclase ATP -> cAMP + PPI cyr1 YDR454C 2.7.4.8 GUK1 guanylate kinase GMP + ATP <-> GDP + ADP guk1_1 YDR454C 2.7.4.8 GUK1 guanylate kinase DGMP + ATP <-> DGDP + ADP guk1_2 YDR454C 2.7.4.8 GUK1 guanylate kinase GMP + DATP <-> GDP + DADP guk1_3 YMR300C 2.4.2.14 ade4 phosphoribosylpyrophosphate amidotransferase PRPP + GLN -> PPI + GLU + PRAM ade4 YGL234W 6.3.4.13 ade5,7 glycinamide ribotide synthetase and aminoimidazole PRAM + ATP + GLY <-> ADP + ade5 ribotide synthetase PI + GAR YDR408C 2.1.2.2 ade8 glycinamide ribotide transformylase GAR + FTHF -> THF + FGAR ade8 YGR061C 6.3.5.3 ade6 5'-phosphoribosylformyl glycinamidine synthetase FGAR + ATP + GLN -> GLU + ADP + ade6 PI + FGAM YGL234W 6.3.3.1 ade5,7 Phosphoribosylformylglycinamide cyclo-ligase FGAM + ATP -> ADP + PI + AIR ade7 YOR128C 4.1.1.21 ade2 phosphoribosylamino-imidazole-carboxylase CAIR <-> AIR + CO2 ade2 YAR015W 6.3.2.6 ade1 phosphoribosyl amino imidazolesuccinocarbozamide CAIR + ATP + ASP <-> ADP + ade1 synthetase PI + SAICAR YLR359W 4.3.2 2 ADE13 5'-Phosphoribosyl-4-(N-succinocarboxamide)-5- SAICAR <-> FUM + AICAR ade13_1 aminoimidazole lyase YLR028C 2.1.2.3 ADE16 5-aminoimidazole-4-carboxamide ribonucleotide AICAR + FTHF <-> THF + PRFICA ade16_1 (AICAR) transformylase\/IMP cyclohydrolase YMR120C 2.1.2.3 ADE17 5-aminomidazole-4-carboxamide ribonucleotide AICAR + FTHF <-> THF + PRFICA ade17_1 (AICAR) transformylase\/IMP cyclohydrolase YLR028C 3.5.4.10 ADE16 5-aminoimidazole-4-carboxamide ribonucleotide PRFICA <-> IMP ade16_2 (AICAR) transformylase\/IMP cyclohydrolase YMR120C 2.1.2.3 ADE17 IMP cyclohydrolase PRFICA <-> IMP ade17_2 YNL220W 6.3.4.4 ade12 adenylosuccinate synthetase IMP + GTP + ASP -> GDP + PI + ASUC ade12 YLR359W 4.3.2.2 ADE13 Adenylosuccinate Lyase ASUC <-> FUM + AMP ade13_2 YAR073W 1.1.1.205 fun63 putative inosine-5'-monophosphate dehydrogenase IMP + NAD -> NADH + XMP fun63 YHR216W 1.1.1.205 pur5 purine excretion IMP + NAD -> NADH + XMP pur5 YML056C 1.1.1.205 IMD4 probable inosine-5'-monophosphate dehydrogenase IMP + NAD -> NADH + XMP prm5 (IMP YLR432W 1.1.1.205 IMD3 probable inosine-5'-monophosphate dehydrogenase IMP + NAD -> NADH + XMP prm4 (IMP YAR075W 1.1.1.205 YAR075W Protein with strong similarity to inosine-5'- IMP + NAD -> NADH + XMP prm6 monophosphate dehydrogenase, frameshifted from YAR073W, possible pseudogene YMR217W 6.3.5.2, GUA1 GMP synthase XMP + ATP + GLN -> GLU + AMP + gua1 6.3.4.1 PPI + GMP YML035C 3.5.4.6 amd1 AMP deaminase AMP -> IMP + NH3 amd1 YGL248W 3.1.4.17 PDE1 3′,5′-Cyclic-nucleotide phosphodiesterase, low affinity cAMP -> AMP pde1 YOR360C 3.1.4.17 pde2 3′,5′-Cyclic-nucleotide phosphodiesterase, high affinity cAMP -> AMP pde2_1 YOR360C 3.1.4.17 pde2 cdAMP -> DAMP pde2_2 YOR360C 3.1.4.17 pde2 cIMP -> IMP pde2_3 YOR360C 3.1.4.17 pde2 cGMP -> GMP pde2_4 YOR360C 3.1.4.17 pde2 cCMP -> CMP pde2_5 YDR530C 2.7.7.53 APA2 5′,5′″-P-1,P-4-tetraphosphate phosphorylase II ADP + ATP -> PI + ATRP apa2 YCL050C 2.7.7.53 apa1 5′,5′″-P-1,P-4-tetraphosphate phosphorylase II ADP + GTP -> PI + ATRP apa1_1 YCL050C 2.7.7.53 apa1 5′,5′″-P-1,P-4-tetraphosphate phosphorylase II GDP + GTP -> PI + GTRP apa1_3 Pyrimidine metabolism YJL130C 2.1.3.2 ura2 Aspartate-carbamoyltransferase CAP + ASP -> CAASP + PI ura2_1 YLR420W 3.5.2.3 ura4 dihydrooratase CAASP <-> DOROA ura4 YKL216W 1.3.3.1 ura1 dihydroorotate dehydrogenase DOROA + O2 <-> H2O2 + OROA ura1_1 YKL216W 1.3.3.1 PYRD Dihydroorotate dehydrogenase DOROA + Qm <-> QH2m + OROA ura1_2 YML106W 2.4.2.10 URA5 Orotate phosphoribosyltransferase 1 OROA + PRPP <-> PPI + OMP ura5 YMR271C 2.4.2.10 URA10 Orotate phosphoribosyltransferase 2 OROA + PRPP <-> PPI + OMP ura10 YEL021W 4.1.1.23 ura3 orotidine-5′-phosphate decarboxylase OMP -> CO2 + UMP ura3 YKL024C 2.7.4.14 URA6 Nucleoside-phosphate kinase ATP + UMP <-> ADP + UDP npk YHR128W 2.4.2.9 fur1 UPRTase, Uracil phosphoribosyltransferase URA + PRPP -> UMP + PPI fur1 YPR062W 3.5.4.1 FCY1 cytosine deaminase CYTS -> URA + NH3 fcy1 — 2.7.1.21 Thymidine (deoxyuridine) kinase DU + ATP -> DUMP + ADP tdk1 — 2.7.1.21 Thymidine (deoxyuridine) kinase DT + ATP -> ADP + DTMP tdk2 YNR012W 2.7.1.48 URK1 Uridine kinase URI + GTP -> UMP + GDP urk1_1 YNR012W 2.7.1.48 URK1 Cytodine kinase CYTD + GTP -> GDP + CMP urk1_2 YNR012W 2.7.1.48 URK1 Uridine kinase, converts ATP and uridine to ADP and URI + ATP -> ADP + UMP urk1_3 UMP YLR209C 2.4.2.4 PNP1 Protein with similarity to human purine nucleoside DU + PI <-> URA + DR1P deoa1 phosphorylase, Thymidine (deoxyuridine) phosphorylase, Purine nucleotide phosphorylase YLR209C 2.4.2.4 PNP1 Protein with similarity to human purine nucleoside DT + PI <-> THY + DR1P deoa2 phosphorylase, Thymidine (deoxyuridine) phosphorylase YLR245C 3.5.4.5 CDD1 Cytidine deaminase CYTD -> URI + NH3 cdd1_1 YLR245C 3.5.4.5 CDD1 Cytidine deaminase DC -> NH3 + DU cdd1_2 YJR057W 2.7.4.9 cdc8 dTMP kinase DTMP + ATP <-> ADP + DTDP cdc8 YDR353W 1.6.4.5 TRR1 Thioredoxin reductase OTHIO + NADPH -> NADP + RTHIO trr1 YHR106W 1.6.4.5 TRR2 mitochondrial thioredoxin reductase OTHIOm + NADPHm -> NADPm + trr2 RTHIOm YBR252W 3.6.1.23 DUT1 dUTP pyrophosphatase (dUTPase) DUTP -> PPI + DUMP dut1 YOR074C 2.1.1.45 cdc21 Thymidylate synthase DUMP + METTHF -> DHF + DTMP cdc21 — 2.7.4.14 Cytidylate kinase DCMP + ATP <-> ADP + DCDP cmka1 — 2.7.4.14 Cytidylate kinase CMP + ATP <-> ADP + CDP cmka2 YHR144C 3.5.4.12 DCD1 dCMP deaminase DCMP <-> DUMP + dcd1 NH3 YBL039C 6.3.4.2 URA7 CTP synthase, highly homologus to URA8 CTP UTP + GLN + ATP -> GLU + CTP + ura7_1 synthase ADP + PI YJR103W 6.3.4.2 URA8 CTP synthase UTP + GLN + ATP -> GLU + CTP + ura8_1 ADP + PI YBL039C 6.3.4.2 URA7 CTP synthase, highly homologus to URA8 CTP ATP + UTP + NH3 - > ADP + PI + CTP ura7_2 synthase YJR103W 6.3.4.2 URA8 CTP synthase ATP + UTP + NH3 -> ADP + PI + CTP ura8_2 YNL292W 4.2.1.70 PUS4 Pseudouridine synthase URA + R5P <-> PURI5P pus4 YPL212C 4.2.1.70 PUS1 intranuclear protein which URA + R5P <-> PURI5P pus1 exhibits a nucleotide-specific intron-dependent tRNA pseudouridine synthase activity YGL063W 4.2.1.70 PUS2 pseudouridine synthase 2 URA + R5P <-> PURI5P pus2 YFL001W 4.2.1.70 deg1 Similar to rRNA methyltransferase (Caenorhabditis URA + R5P <-> PURI5P deg1 elegans) and hypothetical 28 K protein (alkaline endoglucanase gene 5′ region) from Bacillus sp. Salvage Pathways YML022W 2.4.2.7 APT1 Adenine phosphoribosyltransferase AD + PRPP -> PPI + AMP apt1 YDR441C 2.4.2.7 APT2 similar to adenine phosphoribosyltransferase AD + PRPP -> PPI + AMP apt2 YNL141W 3.5.4.4 AAH1 adenine aminohydrolase (adenine deaminase) ADN -> INS + NH3 aah1a YNL141W 3.5.4.4 AAH1 adenine aminohydrolase (adenine deaminase) DA -> DIN + NH3 aah1b YLR209C 2.4.2.1 PNP1 Purine nucleotide phosphorylase, Xanthosine DIN + PI <-> HYXN + DR1P xapa1 phosphorylase YLR209C 2.4.2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide DA + PI <-> AD + DR1P xapa2 phosphorylase YLR209C 2.4.2.1 PNP1 Xanthosine phosphorylase DG + PI <-> GN + DR1P xapa3 YLR209C 2.4.2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide HYXN + RIP <-> INS + PI xapa4 phosphorylase YLR209C 2.4.2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide AD + RIP <-> PI + ADN xapa5 phosphorylase YLR209C 2.4.2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide GN + RIP <-> PI + GSN xapa6 phosphorylase YLR209C 2.4.2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide XAN + RIP <-> PI + XTSINE xapa7 phosphorylase YJR133W 2.4.2.22 XPT1 Xanthine-guanine phosphoribosyltransferase XAN + PRPP -> XMP + PPI gpt1 YDR400W 3.2.2.1 urh1 Purine nucleosidase GSN -> GN + RIB pur21 YDR400W 3.2.2.1 urh1 Purine nucleosidase ADN -> AD + RIB pur11 YJR105W 2.7.1.20 YJR105W Adenosine kinase ADN + ATP -> AMP + ADP prm2 YDR226W 2.7.4.3 adk1 cytosolic adenylate kinase ATP + AMP <-> 2 ADP adk1_1 YDR226W 2.7.4.3 adk1 cytosolic adenylate kinase GTP + AMP <-> ADP + GDP adk1_2 YDR226W 2.7.4.3 adk1 cytosolic adenylate kinase ITP + AMP <-> ADP + IDP adk1_3 YER170W 2.7.4.3 ADK2 Adenylate kinase (mitochondrial GTP: AMP ATPm + AMPm <-> 2 ADPm adk2_1 phosphotransferase YER170W 2.7.4.3 adk2 Adenylate kinase (mitochondrial GTP: AMP GTPm + AMPm <-> ADPm + GDPm adk2_2 phosphotransferase YER170W 2.7.4.3 adk2 Adenylate kinase (mitochondrial GTP: AMP ITPm + AMPm <-> ADPm + IDPm adk2_3 phosphotransferase) YGR180C 1.17.4.1 RNR4 ribonucleotide reductase, small subunit (alt), beta chain YIL066C 1.17.4.1 RNR3 Ribonucleotide reductase (ribonucleoside-diphosphate ADP + RTHIO -> DADP + OTHIO rnr3 reductase) large subunit, alpha chain YJL026W 1.17.4.1 rnr2 small subunit of ribonucleotide reductase, beta chain YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase UDP + ATP <-> UTP + ADP ynk1_1 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase CDP + ATP <-> CTP + ADP ynk1_2 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase DGDP + ATP <-> DGTP + ADP ynk1_3 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase DUDP + ATP <-> DUTP + ADP ynk1_4 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase DCDP + ATP <-> DCTP + ADP ynk1_5 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase DTDP + ATP <-> DTTP + ADP ynk1_6 YKL067W 2.7.4.6 YNK1 Nucleoside-diphosphate kinase DADP + ATP <-> DATP + ADP ynk1_7 YKL067W 2.7.4.6 YNK1 Nucleoside diphosphate kinase GDP + ATP <-> GTP + ADP ynk1_8 YKL067W 2.7.4.6 YNK1 Nucleoside diphosphate kinase IDP + ATP <-> ITP + IDP ynk1_9 — 2.7.4.11 Adenylate kinase, dAMP kinase DAMP + ATP <-> DADP + ADP dampk YNL141W 3.5.4.2 AAH1 Adenine deaminase AD -> NH3 + HYXN yicp — 2.7.1.73 Inosine kinase INS + ATP -> IMP + ADP gsk1 — 2.7.1.73 Guanosine kinase GSN + ATP -> GMP + ADP gsk2 YDR399W 2.4.2.8 HPT1 Hypoxanthine phosphoribosyltransferase HYXN + PRPP -> PPI + IMP hpt1_1 YDR399W 2.4.2.8 HPT1 Hypoxanthine phosphoribosyltransferase GN + PRPP -> PPI + GMP hpt1_2 — 2.4.2.3 Uridine phosphorylase URI + PI <-> URA + RIP udp YKL024C 2.1.4.- URA6 Uridylate kinase UMP + ATP <-> UDP + ADP pyrh1 YKL024C 2.1.4.- URA6 Uridylate kinase DUMP + ATP <-> DUDP + ADP pyrh2 — 3.2.2.10 CMP glycosylase CMP -> CYTS + R5P cmpg YHR144C 3.5.4.13 DCD1 dCTP deaminase DCTP -> DUTP + NH3 dcd — 3.1.3.5 5′-Nucleotidase DUMP -> DU + PI usha1 — 3.1.3.5 5′-Nucleotidase DTMP -> DT + PI usha2 — 3.1.3.5 5′-Nucleotidase DAMP -> DA + PI usha3 — 3.1.3.5 5′-Nucleotidase DGMP -> DG + PI usha4 — 3.1.3.5 5′-Nucleotidase DCMP -> DC + PI usha5 — 3.1.3.5 5′-Nucleotidase CMP -> CYTD + PI usha6 — 3.1.3.5 5′-Nucleotidase AMP -> PI + ADN usha7 — 3.1.3.5 5′-Nucleotidase GMP -> PI + GSN usha8 — 3.1.3.5 5′-Nucleotidase IMP -> PI + INS usha9 — 3.1.3.5 5′-Nucleotidase XMP -> PI + XTSINE usha12 — 3.1.3.5 5′-Nucleotidase UMP -> PI + URI usha11 YER070W 1.17.4.1 RNR1 Ribonucleoside-diphosphate reductase ADP + RTHIO -> DADP + OTHIO rnr1_1 YER070W 1.17.4.1 RNR1 Ribonucleoside-diphosphate reductase GDP + RTHIO -> DGDP + OTHIO rnr1_2 YER070W 1.17.4.1 RNR1 Ribonucleoside-diphosphate reductase CDP + RTHIO -> DCDP + OTHIO rnr1_3 YER070W 1.17.4.1 RNR1 Ribonucleoside-diphosphate reductase UDP + RTHIO -> OTHIO + DUDP rnr1_4 — 1.17.4.2 Ribonucleoside-triphosphate reductase ATP + RTHIO -> DATP + OTHIO nrdd1 — 1.17.4.2 Ribonucleoside-triphosphate reductase GTP + RTHIO -> DGTP + OTHIO nrdd2 — 1.17.4.2 Ribonucleoside-triphosphate reductase CTP + RTHIO -> DCTP + OTHIO nrdd3 — 1.17.4.2 Ribonucleoside-triphosphate reductase UTP + RTHIO -> OTHIO + DUTP nrdd4 3.6.1.- Nucleoside triphosphatase GTP -> GSN + 3 PI mutt1 3.6.1.- Nucleoside triphosphatase DGTP -> DG + PI mutt2 YML035C 3.2.2.4 AMD1 AMP deaminase AMP -> AD + R5P amn YBR284W 3.2.2.4 YBR284W Protein with similarity to AMP deaminase AMP -> AD + R5P amn1 YJL070C 3.2.2.4 YJL070C Protein with similarity to AMP deaminase AMP -> AD + R5P amn2 Amino Acid Metabolism Glutamate Metabolism (Aminosugars met) YMR250W 4.1.1.15 GAD1 Glutamate decarboxylase B GLU -> GABA + CO2 btn2 YGR019W 2.6.1.19 uga1 Aminobutyrate aminotransaminase 2 GABA + AKG -> SUCCSAL + GLU uga1 YBR006w 1.2.1.16 YBR006w Succinate semialdehyde dehydrogenase-NADP SUCCSAL + NADP -> SUCC + NADPH gabda YKL104C 2.6.1.16 GFA1 Glutamine_fructose-6-phosphate amidotransferase F6P + GLN -> GLU + GA6P gfa1 (glucoseamine-6-phosphate synthase) YFL017C 2.3.1.4 GNA1 Glucosamine-phosphate N-acetyltransferase ACCOA + GA6P <-> COA + NAGA6P gna1 YEL058W 5.4.2.3 PCM1 Phosphoacetylglucosamine Mutase NAGA1P <-> NAGA6P pcm1a YDL103C 2.7.7.23 QRI1 N-Acetylglucosamine-1-phosphate-uridyltransferase UTP + NAGA1P <-> UDPNAG + PPI qrt1 YBR023C 2.4.1.16 chs3 chitin synthase 3 UDPNAG -> CHIT + UDP chs3 YBR038W 2.4.1.16 CHS2 chitin synthase 2 UDPNAG -> CHIT + UDP chs2 YNL192W 2.4.1.16 CHS1 chitin synthase 2 UDPNAG -> CHIT + UDP chs1 YHR037W 1.5.1.12 put2 delta-1-pyrroline-5-carboxylate dehydrogenase GLUGSALm + NADPm -> NADPHm + put2_1 GLUm P5Cm + put2 NADm -> NADHm + GLUm YDL171C 1.4.1.14 GLT1 Glutamate synthase (NADH) AKG + GLN + NADH -> NAD + 2 GLU glt1 YDL215C 1.4.1.4 GDH2 glutamate dehydrogenase GLU + NAD -> AKG + NH3 + NADH gdh2 YAL062W 1.4.1.4 GDH3 NADP-linked glutamate dehydrogenase AKG + NH3 + gdh3 NADPH <-> GLU + NADP YOR375C 1.4.1.4 GDH1 NADP-specific glutamate dehydrogenase AKG + NH3 + gdh1 NADPH <-> GLU + NADP YPR035W 6.3.1.2 gln1 glutamine synthetase GLU + NH3 + ATP -> GLN + ADP + PI gln1 YEL058W 5.4.2.3 PCM1 Phosphoglucosamine mutase GA6P <-> GA1P pcm1b — 3.5.1.2 Glutaminase A GLN -> GLU + NH3 glnasea — 3.5.1.2 Glutaminase B GLN -> GLU + NH3 glnaseb Glucosamine — 5.3.1.10 Glucosamine-6-phosphate deaminase GA6P -> F6P + NH3 nagb Arabinose YBR149W 1.1.1.117 ARA1 D-arabinose 1-dehydrogenase (NAD(P)+) ARAB + ara1_1 NAD -> ARABLAC + NADH YBR149W 1.1.1.117 ARA1 D-arabmose 1-dehydrogenase (NAD(P)+) ARAB + NADP -> ARABLAC + ara1_2 NADPH Xylose YGR194C 2.7.1.17 XKS1 Xylulokinase XUL + ATP -> X5P + ADP xks1 Mannitol — 1.1.1.17 Mannitol-1-phosphate 5-dehydrogenase MNT6P + NAD <-> F6P + NADH mtld Alanine and Aspartate Metabolism YKL106W 2.6.1.1 AAT1 Asparate transaminase OAm + GLUm <-> ASPm + AKGm aat1_1 YLR027C 2.6.1.1 AAT2 Asparate transaminase OA + GLU <->ASP + AKG aat2_1 YAR035W 2.3.1.7 YAT1 Carnitine O-acetyltransferase COAm + ACARm -> ACCOAm + CARm yat1 YML042W 2.3.1.7 CAT2 Carnitine O-acetyltransferase ACCOA + CAR -> COA + ACAR cat2 YDR111C 2.6.1.2 YDR111C putative alanine transaminase PYR + GLU <-> AKG + ALA alab YLR089C 2.6.1.2 YLR089C alanine aminotransferase, mitochondrial precursor PYRm + cfx2 (glutamic-- GLUm <-> AKGm + ALAm YPR145W 6.3.5.4 ASN1 asparagine synthetase ASP + ATP + GLN -> GLU + ASN + asn1 AMP + PP1 YGR124W 6.3.5.4 ASN2 asparagine synthetase ASP + ATP + GLN -> GLU + ASN + asn2 AMP + PP1 YLL062C 2.1.1.10 MHT1 Putative cobalamin-dependent homocysteine S- SAM + HCYS -> SAH + MET mht1 methyltransferase, Homocysteine S-methyltransferase YPL273W 2.1.1.10 SAM4 Putative cobalamin-dependent homocysteine S- SAM + HCYS -> SAH + MET sam4 methyltransferase Asparagine YCR024c 6.1.1.22 YCR024c asn-tRNA synthetase, mitochondrial ATPm + ASPm + TRNAm -> AMPm + rnas PP1m + ASPTRNAm YHR019C 6.1.1.23 DED81 asn-tRNA synthetase ATP + ASP + TRNA -> AMP + ded81 PPI + ASPTRNA YLR155C 3.5.1.1 ASP3-1 Asparaginase, extracellular ASN -> ASP + NH3 asp3_1 YLR157C 3.5.1.1 ASP3-2 Asparaginase, extracellular ASN -> ASP + NH3 asp3_2 YLR158C 3.5.1.1 ASP3-3 Asparaginase, extracellular ASN -> ASP + NH3 asp3_3 YLR160C 3.5.1.1 ASP3-4 Asparaginase, extracellular ASN -> ASP + NH3 asp3_4 YDR321W 3.5.1.1 asp1 Asparaginase ASN -> ASP + NH3 asp1 Glycine, serine and threonine metabolism YER081W 1.1.1.95 ser3 Phosphoglycerate dehydrogenase 3PG + NAD -> NADH + PHP ser3 YIL074C 1.1.1.95 ser33 Phosphoglycerate dehydrogenase 3PG + NAD -> NADH + PHP ser33 YOR184W 2.6.1.52 ser1 phosphoserine transaminase PHP + GLU -> AKG + 3PSER ser1_1 YGR208W 3.1.3.3 ser2 phosphoserine phosphatase 3PSER -> PI + SER ser2 YBR263W 2.1.2.1 SHM1 Glycine hydroxymethyltransferase THFm + SERm <-> GLYm + METTHFm shm1 YLR058C 2.1.2.1 SHM2 Glycine hydroxymethyltransferase THF + SER <-> GLY + METTHF shm2 YFL030W 2.6.1.44 YFL030W Putative alanine glyoxylate aminotransferase (serine ALA + GLX <-> PYR + GLY agt pyruvate aminotransferase) YDR019C 2.1.2.10 GCV1 glycine cleavage T protein (T subunit of glycine GLYm + THFm + gcv1_1 decarboxylase complex NADm -> METTHFm + NADHm + CO2 + NH3 YDR019C 2.1.2.10 GCV1 glycine cleavage T protein (T subunit of glycine GLY + THF + NAD-> METTHF + gcv1_2 decarboxylase complex NADH + CO2 + NH3 YER052C 2.7.2.4 hom3 Aspartate kinase, Aspartate kinase I, II, III ASP + ATP -> ADP + BASP hom3 YDR158W 1.2.1.11 hom2 aspartic beta semi-aldehyde dehydrogenase, Aspartate BASP + NADPH -> NADP + hom2 semialdehyde dehydrogenase PI + ASPSA YJR139C 1.1.1.3 hom6 Homoserine dehydrogenase I ASPSA + NADH -> NAD + HSER hom6_1 YJR139C 1.1.1.3 hom6 Homoserine dehydrogenase I ASPSA + NADPH -> NADP + HSER hom6_2 YHR025W 2.7.1.39 thr1 homoserine kinase HSER + ATP -> ADP + PHSER thr1 YCR053W 4.2.99.2 thr4 threonine synthase PHSER -> PI + THR thr4_1 YGR155W 4.2.1.22 CYS4 Cystathionine beta-synthase SER + HCYS -> LLCT cys4 YEL046C 4.1.2.5 GLY1 Threonine Aldolase GLY + ACAL -> THR gly1 YMR189W 1.4.4.2 GCV2 Glycine decarboxylase complex (P-subunit), glycine GLYm + LIPOm <-> SAPm + CO2m gcv2 synthase (P-subunit), Glycine cleavage system (P- subunit) YCL064C 4.2.1.16 cha1 threonine deaminase THR -> NH3 + OBUT cha1_1 YER086W 4.2.1.16 ilv1 L-Serine dehydratase THRm -> NH3m + OBUTm ilv1 YCL064C 4.2.1.13 cha1 catabolic serine (threonine) dehydratase SER -> PYR + NH3 cha1_2 YIL167W 4.2.1.13 YIL167W catabolic serine (threonine) dehydratase SER -> PYR + NH3 sdl1 — 1.1.1.103 Threonine dehydrogenase THR + NAD -> GLY + AC + NADH tdh1c Methionine metabolism YFR055W 4.4.1.8 YFR055W Cystathionine-b-lyase LLCT -> HCYS + PYR + NH3 metc YER043C 3.3.1.1 SAH1 putative S-adenosyl-L-homocysteine hydrolase SAH -> HCYS + ADN sah1 YER091C 2.1.1.14 met6 vitamin B12-(cobalamin)-independent isozyme of HCYS + MTHPTGLU -> THPTGLU + met6 methionine synthase (also called N5- MET methyltetrahydrofolate homocysteine methyltransferase or 5-methyltetrahydropteroyl triglutamate homocysteine methyltransferase) — 2.1.1.13 Methionine synthase HCYS + MTHF -> THF + MET met6 _2 YAL012W 4.4.1.1 cys3 cystathionine gamma-lyase LLCT -> CYS + NH3 + OBUT cys3 YNL277W 2.3.1.31 met2 homoserine O-trans-acetylase ACCOA + HSER <-> COA + OAHSER met2 YLR303W 4.2.99.10 MET17 O-Acetylhomoserine (thiol)-lyase OAHSER + METH -> MET + AC met17_1 YLR303W 4.2.99.8 MET17 O-Acetylhomoserine (thiol)-lyase OAHSER + H2S -> AC + HCYS met17_2 YLR303W 4.2.99.8, met17 O-acetylhomoserine sulfhydrylase (OAH SHLase), OAHSER + H2S -> AC + HCYS met17_3 4.2.99.10 converts O-acetylhomoserine into homocysteine YML082W 4.2.99.9 YML082W putative cystathionine gamma-synthase OSLHSER <-> SUCC + OBUT + NH4 met17h YDR502C 2.5.1.6 sam2 S-adenosylmethionine synthetase MET + ATP -> PPI + PI + SAM sam2 YLR180W 2.5.1.6 sam1 S-adenosylmethionine synthetase MET + ATP -> PPI + PI + SAM sam1 YLR172C 2.1.1.98 DPH5 Diphthine synthase SAM + CALH -> SAH + DPTH dph5 Cysteine Biosynthesis YJR010W 2.7.7.4 met3 ATP sulfurylase SLF + ATP -> PPI + APS met3 YKL001C 2.7.1.25 met14 adenylylsulfate kinase APS + ATP -> ADP + PAPS met14 YFR030W 1.8.1.2 met10 sulfite reductase H2SO3 + 3 NADPH <-> H2S + 3 NADP met10 — 2.3.1.30 Serine transacetylase SER + ACCOA -> COA + ASER cys1 YGR012W 4.2.99.8 YGR012W putative cysteine synthase (O-acetylserine ASER + H2S -> AC + CYS sul11 sulfhydrylase) (O- YOL064C 3.1.3.7 MET22 3′–5′ Bisphosphate nucleotidase PAP -> AMP + PI met22 YPR167C 1.8.99.4 MET16 PAPS Reductase PAPS + RTHIO-> OTHIO + met16 H2SO3 + PAP YCL050C 2.7.7.5 apa1 diadenosine 5′,5″′-P1,P4- ADP + SLF <-> PI + APS apa1_2 tetraphosphate phosphorylase I Branched Chain Amino Acid Metabolism (Valine, Leucine and Isoleucine) YHR208W 2.6.1.42 BAT1 Branched chain amino acid aminotransferase OICAPm + GLUm <-> AKGm + LEUm bat1_1 YHR208W 2.6.1.42 BAT1 Branched chain amino acid aminotransferase OMVALm + GLUm <-> AKGm + ILEm bat1_2 YJR148W 2.6.1.42 BAT2 branched-chain amino acid OMVAL + GLU <-> AKG + ILE bat2_1 transaminase, highly similar to mammalian ECA39, which is regulated by the oncogene myc YJR148W 2.6.1.42 BAT2 Branched chain amino acid aminotransferase OIVAL + GLU <-> AKG + VAL bat2_2 YJR148W 2.6.1.42 BAT2 branched-chain amino acid OICAP + GLU <-> AKG + LEU bat2_3 transaminase, highly similar to mammalian ECA39, which is regulated by the oncogene myc YMR108W 4.1.3.18 ilv2 Acetolactate synthase, large subunit OBUTm + PYRm -> ABUTm + CO2m ilv2_1 YCL009C 4.1.3.18 ILV6 Acetolactate synthase, small subunit YMR108W 4.1.3.18 ilv2 Acetolactate synthase, large subunit 2 PYRm -> CO2m + ACLACm ilv2_2 YCL009C 4.1.3.18 ILV6 Acetolactate synthase, small subunit YLR355C 1.1.1.86 ilv5 Keto-acid reductoisomerase ACLACm + NADPHm -> NADPm + ilv5_1 DHVALm YLR355C 1.1.1.86 ilv5 Keto-acid reductoisomerase ABUTm + NADPHm -> NADPm + ilv5_2 DHMVAm YJR016C 4.2.1.9 ilv3 Dihydroxy acid dehydratase DHVALm -> OIVALm ilv3_1 YJR016C 4.2.1.9 ilv3 Dihydroxy acid dehydratase DHMVAm -> OMVALm ilv3_2 YNL104C 4.1.3.12 LEU4 alpha-isopropylmalate synthase (2-Isopropylmalate ACCOAm + OIVALm -> COAm + leu4 Synthase) IPPMALm YGL009C 4.2.1.33 leu1 Isopropylmalate isomerase CBHCAP <-> IPPMAL leu1_1 YGL009C 4.2.1.33 leu1 isopropylmalate isomerase PPMAL <-> IPPMAL leu1_2 YCL018W 1.1.1.85 leu2 beta-IPM (isopropylmalate) dehydrogenase IPPMAL + NAD -> NADH + leu2 OICAP + CO2 Lysine biosynthesis/degradation — 4.2.1.79 2-Methylcitrate dehydratase HCITm <-> HACNm lys3 YDR234W 4.2.1.36 lys4 Homoaconitate hydratase HICITm <-> HACNm lys4 YIL094C 1.1.1.155 LYS12 Homoisocitrate dehydrogenase (Strathern 1.1.1.87) HICITm + NADm<-> OXAm + lys12 CO2m + NADHm — non-enzymatic OXAm <-> CO2m + AKAm lys12b — 2.6.1.39 2-Aminoadipate transaminase AKA + GLU <-> AMA + amit AKG YBR115C 1.2.1.31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA + NADPH + ATP -> AMASA + lys2_1 subunit NADP + AMP + PPI YGL154C 1.2.1.31 lys5 L-Aminoadipate-semialdehyde dehydrogenase, small subunit YBR115C 1.2.1.31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA + NADH + ATP -> AMASA + lys2_2 subunit NAD + AMP + PPI YGL154C 1.2.1.31 lys5 L-Aminoadipate-semialdehyde dehydrogenase, small subunit YNR050C 1.5.1.10 lys9 Saccharopine dehydrogenase (NADP+, L-glutamate GLU + AMASA + NADPH <-> SACP + lys9 forming) NADP YIR034C 1.5.1.7 lys1 Saccharopine dehydrogenase SACP + NAD <-> LYS + AKG + NADH lys1a (NAD+, L-lysine forming) YDR037W 6.1.1.6 krs1 lysyl-tRNA synthetase, cytosolic ATP + LYS + LTRNA -> AMP + krs1 PPI + LLTRNA YNL073W 6.1.1.6 msk1 lysyl-tRNA synthetase, mitochondrial ATPm + LYSm + LTRNAm -> AMPm + msk1 PPIm + LLTRNAm YDR368W 1.1.1.- YPRI similar to aldo-keto reductase Arginine metabolism YMR062C 2.3.1.1 ECM40 Amino-acid N-acetyltransferase GLUm + ACCOAm -> COAm + ecm40_1 NAGLUm YER069W 2.7.2.8 arg5 Acetylglutamate kinase NAGLUm + ATPm -> ADPm + arg6 NAGLUPm YER069W 1.2.1.38 arg5 N-acetyl-gamma-glutamyl-phosphate reductase and NAGLUPm + NADPHm -> NADPm + arg5 acetylglutamate kinase PIm + NAGLUSm YOL140W 2.6.1.11 arg8 Acetylornithine aminotransferase NAGLUSm + GLUm -> AKGm + arg8 NAORNm YMR062C 2.3.1.35 ECM40 Glutamate N-acetyltransferase NAORNm + GLUm -> ORNm + ecm40_2 NAGLUm YJL130C 6.3.5.5 ura2 carbamoyl-phophate synthetase, aspartate GLN + 2 ATP + CO2 -> GLU + CAP + 2 ura2_2 transcarbamylase, and glutamine amidotransferase ADP + PI YJR109C 6.3.5.5 CPA2 carbamyl phosphate synthetase, large chain GLN + 2 ATP + CO2 -> GLU + cpa2 CAP + 2 ADP + PI YOR303W 6.3.5.5 cpa1 Carbamoyl phosphate synthetase, samll chain, arginine specific YJL088W 2.1.3.3 arg3 Ornithine carbamoyltransferase ORN + CAP -> CITR + PI arg3 YLR438W 2.6.1.13 car2 Ornithine transaminase ORN + AKG -> GLUGSAL + GLU car2 YOL058W 6.3.4.5 arg1 arginosuccinate synthetase CITR + ASP + ATP <-> AMP + arg1 PPI + ARGSUCC YHR018C 4.3.2.1 arg4 argininosuccinate lyase ARGSUCC <-> FUM + ARG arg4 YKL184W 4.1.1.17 spe1 Ornithine decarboxylase ORN -> PTRSC + CO2 spe1 YOL052C 4.1.1.50 spe2 S-adenosylmethionine decarboxylase SAM <-> DSAM + CO2 spe2 YPR069C 2.5.1.16 SPE3 putrescine aminopropyltransferase (spermidine PTRSC + SAM -> SPRMD + 5MTA spe3 synthase) YLR146C 2.5.1.22 SPE4 Spermine synthase DSAM + SPRMD -> 5MTA + SPRM spe4 YDR242W 3.5.1.4 AMD2 Amidase GBAD -> GBAT + NH3 amd2_1 YMR293C 3.5.1.4 YMR293C Probable Amidase GBAD -> GBAT + NH3 amd YPL111W 3.5.3.1 car1 arginase ARG -> ORN + UREA car1 YDR341C 6.1.1.19 YDR341C arginyl-tRNA synthetase ATP + ARG + ATRNA -> AMP + atrna PPI + ALTRNA YHR091C 6.1.1.19 MSR1 arginyl-tRNA synthetase ATP + ARG + ATRNA -> AMP + msr1 PPI + ALTRNA YHR068W 1.5.99.6 DYS1 deoxyhypusine synthase SPRMD + Qm -> DAPRP + QH2m dys1 Histidine metabolism YER055C 2.4.2.17 his1 ATP phosphoribosyltransferase PRPP + ATP -> PPI + PRBATP his1 YCL030C 3.6.1.31 his4 phosphoribosyl-AMP cyclohydrolase/phosphoribosyl- PRBATP -> PPI + PRBAMP his4_1 ATP pyrophosphohydrolase/histidinol dehydrogenase YCL030C 3.5.4.19 his4 histidinol dehydrogenase PRBAMP -> PRFP his4_2 YIL020C 5.3.1.16 his6 phosphoribosyl-5-amino-1-phosphoribosyl-4- PRFP -> PRLP his6 imidazolecarboxiamide isomerase YOR202W 4.2.1.19 his3 imidazoleglycerol-phosphate dehydratase DIMGP -> IMACP his3 YIL116W 2.6.1.9 his5 histidinol-phosphate aminotransferase IMACP + GLU -> AKG + HISOLP his5 YFR025C 3.1.3.15 his2 Histidinolphosphatase HISOLP -> PI + HISOL his2 YCL030C 1.1.1.23 his4 phosphoribosyl-AMP cyclohydrolase/phosphoribosyl- HISOL + 2 NAD -> HIS + 2 NADH his4_3 ATP pyrophosphohydrolase/histidinol dehydrogenase YBR248C 2.4.2.- his7 glutamine amidotransferase cyclase PRLP + GLN -> GLU + his7 AICAR + DIMGP YPR033C 6.1.1.21 hts1 histidyl-tRNA synthetase ATP + HIS + HTRNA -> AMP + hts1 PPI + HHTRNA YBR034C 2.1.1.- hmt1 hnRNP arginine N-methyltransferase SAM + HIS -> SAH + MHIS hmt1 YCL054W 2.1.1.- spb1 putative RNA methyltransferase YML110C 2.1.1.- coq5 ubiquinone biosynthesis methlytransferase COQ5 YOR201C 2.1.1.- pet56 rRNA (guanosine-2′-O-)-methyltransferase YPL266W 2.1.1.- dim1 dimethyladenosine transferase Phenylalanine, tyrosine and tryptophan biosynthesis (Aromatic Amino Acids) YBR249C 4.1.2.15 ARO4 3-deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) E4P + PEP -> PI + 3DDAH7P aro4 synthase isoenzyme YDR035W 4.1.2.15 ARO3 DAHP synthase\; a.k.a. phospho-2-dehydro-3- E4P + PEP -> PI + 3DDAH7P aro3 deoxyheptonate aldolase, phenylalanine-inhibited\; phospho-2-keto-3-deoxyheptonate aldolase\; 2-dehydro- 3-deoxyphosphoheptonate aldolase\, 3-deoxy-D- arabine-heptulosonate-7-phosphate synthase YDR127W 4.6.1.3 aro1 pentafunctional arom polypeptide (contains: 3- 3DDAH7P -> DQT + PI aro1_1 dehydroquinate synthase, 3-dehydroquinate dehydratase (3-dehydroquinase), shikimate 5-dehydrogenase, shikimate kinase, and epsp synthase) YDR127W 4.2.1.10 aro1 3-Dehydroquinate dehydratase DQT -> DHSK aro1_2 YDR127W 1.1.1.25 aro1 Shikimate dehydrogenase DHSK + NADPH -> SME + NADP aro1_3 YDR127W 2.7.1.71 aro1 Shikimate kinase I, II SME + ATP -> ADP + SME5P aro1_4 YDR127W 2.5.1.19 aro1 3-Phosphoshikimate-1-carboxyvinyltransferase SME5P + PEP -> 3PSME + PI aro1_5 YGL148W 4.6.1.4 aro2 Chorismate synthase 3PSME -> PI + CHOR aro2 YPR060C 5.4.99.5 aro7 Chorismate mutase CHOR -> PHEN aro7 YNL316C 4.2.1.51 pha2 prephenate dehydratase PHEN -> CO2 + PHPYR pha2 YHR137W 2.6.1.- ARO9 putative aromatic amino acid aminotransferase II PHPYR + GLU <-> AKG + PHE aro9_1 YBR166C 1.3.1.13 tyr1 Prephenate dehydrogenase (NADP+) PHEN + NADP -> 4HPP + tyr1 CO2 + NADPH YGL202W 2.6.1.- ARO8 aromatic amino acid aminotransferase I 4HPP + GLU -> AKG + TYR aro8 YHR137W 2.6.1.- ARO9 aromatic amino acid aminotransferase II 4HPP + GLU -> AKG + TYR aro9_2 — 1.3.1.12 Prephanate dehydrogenase PHEN + NAD -> 4HPP + CO2 + NADH tyra2 YER090W 4.1.3.27 trp2 Anthranilate synthase CHOR + GLN -> GLU + PYR + AN trp2_1 YKL211C 4.1.3.27 trp3 Anthranilate synthase CHOR + GLN -> GLU + PYR + AN trp3_1 YDR354W 2.4.2.18 trp4 anthranilate phosphoribosyl transferase AN + PRPP -> PPI + NPRAN trp4 YDR007W 5.3.1.24 trp1 n-(5′-phosphoribosyl)-anthranilate isomerase NPRAN -> CPAD5P trp1 YKL211C 4.1.1.48 trp3 Indoleglycerol phosphate synthase CPAD5P -> CO2 + IGP trp3_2 YGL026C 4.2.1.20 trp5 tryptophan synthetase IGP + SER -> T3PI + TRP trp5 YDR256C 1.11.1.6 CTA1 catalase A 2 H2O2 -> O2 cta1 YGR088W 1.11.1.6 CTT1 cytoplasmic catalase T 2 H2O2 -> O2 ctt1 YKL106W 2.6.1.1 AAT1 Asparate aminotransferase 4HPP + GLU <-> AKG + TYR aat1_2 YLR027C 2.6.1.1 AAT2 Asparate aminotransferase 4HPP + GLU <-> AKG + TYR aat2_2 YMR170C 1.2.1.5 ALD2 Cytosolic aldeyhde dehydrogenase ACAL + NAD -> NADH + AC ald2 YMR169C 1.2.1.5 ALD3 strong similarity to aldehyde dehydrogenase ACAL + NAD -> NADH + AC ald3 YOR374W 1.2.1.3 ALD4 mitochondrial aldehyde dehydrogenase ACALm + NADm -> NADHm + ACm ald4_1 YOR374W 1.2.1.3 ALD4 mitochondrial aldehyde dehydrogenase ACALm + NADPm -> NADPHm + ACm ald4_2 YER073W 1.2.1.3 ALD5 mitochondrial Aldehyde Dehydrogenase ACALm + NADPm -> NADPHm + ACm ald5_1 YPL061W 1.2.1.3 ALD6 Cytosolic Aldehyde Dehydrogenase ACAL + NADP -> NADPH + AC ald6 YJR078W 1.13.11.11 YJR078W Protein with similarity to indoleamine 2,3- TRP + O2 -> FKYN tdo2 dioxygenases, which catalyze conversion of tryptophan and other indole derivatives into kynurenines, Tryptophan 2,3-dioxygenase — 3.5.1.9 Kynurenine formamidase FKYN -> FOR + KYN kfor YLR231C 3.7.1.3 YLR231C probable kynureninase (L-kynurenine hydrolase) KYN -> ALA + AN kynu_1 YBL098W 1.14.13.9 YBL098W Kynurenine 3-hydroxylase, NADPH-dependent flavin KYN + NADPH + O2 -> HKYN + kmo monooxygenase that catalyzes the hydroxylation of NADP kynurenine to 3-hydroxykynurenine in tryptophan degradation and nicotinic acid synthesis, Kynurenine 3-monooxygenase YLR231C 3.7.1.3 YLR231C probable kynureninase (L-kynurenine hydrolase) HKYN -> HAN + ALA kynu_2 YJR025C 1.13.11.6 BNA1 3-hydroxyanthranilate 3,4-dioxygenase (3-HAO) (3- HAN + O2 -> CMUSA bna1 hydroxyanthranilic acid dioxygenase) (3- hydroxyanthranilatehydroxyanthranilic acid dioxygenase) (3-hydroxyanthranilate oxygenase) — 4.1.1.45 Picolinic acid decarboxylase CMUSA -> CO2 + AM6SA aaaa — 1.2.1.32 AM6SA + NAD -> AMUCO + NADH aaab — 1.5.1.- AMUCO + NADPH -> AKA + aaac NADP + NH4 — 1.3.11.27 4-Hydroxyphenylpyruvate dioxygenase 4HPP + O2 -> HOMOGEN + CO2 tyrdega — 1.13.11.5 Homogentisate 1,2-dioxygenase HOMOGEN + O2 -> MACAC tyrdegb — 5.2.1.2 Maleyl-acetoacetate isomerase MACAC -> FUACAC tyrdegc — 3.7.1.2 Fumarylacetoacetase FUACAC -> FUM + ACTAC trydegd YDR268w 6.1.1.2 MSW1 tryptophanyl-tRNA synthetase, mitochondrial ATPm + TRPm + TRNAm -> AMPm + msw1 PPIm + TRPTRNAm YDR242W 3.5.1.4 AMD2 putative amidase PAD -> PAC + NH3 amd2_2 YDR242W 3.5.1.4 AMD2 putative amidase IAD -> IAC + NH3 amd2_3 — 2.6.1.29 Diamine transaminase SPRMD + ACCOA -> ASPERMD + spra COA — 1.5.3.11 Polyamine oxidase ASPERMD + O2 -> APRUT + sprb APROA + H2O2 — 1.5.3.11 Polyamine oxidase APRUT + O2 -> GABAL + sprc APROA + H2O2 — 2.6.1.29 Diamine transaminase SPRM + ACCOA -> ASPRM + sprd COA — 1.5.3.11 Polyamine oxidase ASPRM + O2 -> ASPERMD + spre APROA + H2O2 Proline biosynthesis YDR300C 2.7.2.11 pro1 gamma-glutamyl kinase, glutamate kinase GLU + ATP -> ADP + pro1 GLUP YOR323C 1.2.1.41 PRO2 gamma-glutamyl phosphate reductase GLUP + NADH -> NAD + PI + pro2_1 GLUGSAL YOR323C 1.2.1.41 pro2 gamma-glutamyl phosphate reductase GLUP + NADPH -> NADP + PI + pro2_2 GLUGSAL — spontaneous conversion (Strathern) GLUGSAL <-> P5C gps1 — spontaneous conversion (Strathern) GLUGSALm <-> P5Cm gps2 YER023W 1.5.1.2 pro3 Pyrroline-5-carboxylate reductase P5C + NADPH -> PRO + NADP pro3_1 YER023W 1.5.1.2 pro3 Pyrroline-5-carboxylate reductase PHC + NADPH -> HPRO + NADP pro3_3 YER023W 1.5.1.2 pro3 Pyrroline-5-carboxylate reductase PHC + NADH -> HPRO + NAD pro3_4 YLR142W 1.5.3.- PUT1 Proline oxidase PROm + NADm -> P5Cm + NADHm pro3_5 Metabolism of Other Amino Acids beta-Alanine metabolism 1.2.1.3 aldehyde dehydrogenase, mitochondrial 1 GABALm + NADm -> GABAm + ald1 NADHm YER073W 1.2.1.3 ALD5 mitochondrial Aldehyde Dehydrogenase LACALm + NADm <-> LLACm + ald5_2 NADHm Cyanoamino acid metabolism YJL126W 3.5.5.1 NIT2 NITRILASE APROP -> ALA + NH3 nit2_1 YJL126W 3.5.5.1 NIT2 NITRILASE ACYBUT -> GLU + NH3 nit2_2 Proteins, Peptides and Aminoacids Metabolism YLR195C 2.3.1.97 nmt1 Glycylpeptide N-tetradecanoyltransferase TCOA + GLP -> COA + TGLP nmt1 YDL040C 2.3.1.88 nat1 Peptide alpha-N-acetyltransferase ACCOA + PEPD -> COA + APEP nat1 YGR147C 2.3.1.88 NAT2 Peptide alpha-N-acetyltransferase ACCOA + PEPD -> COA + APEP nat2 Glutathione Biosynthesis YJL101C 6.3.2.2 GSH1 gamma-glutamylcysteine synthetase CYS + GLU + ATP -> GC + PI + ADP gsh1 YOL049W 6.3.2.3 GSH2 Glutathione Synthetase GLY + GC + ATP -> RGT + PI + ADP gsh2 YBR244W 1.11.1.9 GPX2 Glutathione peroxidase 2 RGT + H2O2 <-> OGT gpx2 YIR037W 1.11.1.9 HYR1 Glutathione peroxidase 2 RGT + H2O2 <-> OGT hyr1 YKL026C 1.11.1.9 GPX1 Glutathione peroxidase 2 RGT + H2O2 <-> OGT gpx1 YPL091W 1.6.4.2 GLR1 Glutathione oxidoreductase NADPH + OGT -> NADP + RGT gir1 YLR299W 2.3.2.2 ECM38 gamma-glutamyltranspeptidase RGT + ALA -> CGLY + ALAGLY ecm38 Metabolism of Complex Carbohydrates Starch and sucrose metabolism YGR032W 2.4.1.34 GSC2 1,3-beta-Glucan synthase UDPG -> 13GLUCAN + UDP gsc2 YLR342W 2.4.1.34 FKS1 1,3-beta-Glucan synthase UDPG -> 13GLUCAN + UDP fks1 YGR306W 2.4.1.34 FKS3 Protein with similarity to Fks1p and Gsc2p UDPG -> 13GLUCAN + UDP fks3 YDR261C 3.2.1.58 exg2 Exo-1,3-b-glucanase 13GLUCAN -> GLC exg2 YGR282C 3.2.1.58 BGL2 Cell wall endo-beta-1,3-glucanase 13GLUCAN -> GLC bgl2 YLR300W 3.2.1.58 exg1 Exo-1,3-beta-glucanase 13GLUCAN -> GLC exg1 YOR190W 3.2.1.58 spr1 sporulation-specific exo-1,3-beta-glucanase 13GLUCAN -> GLC spr1 Glycoprotein Biosynthesis/Degradation YMR013C 2.7.1.108 sec59 Dolichol kinase CTP + DOL -> CDP + DOLP sec59 YPR183W 2.4.1.83 DPM1 Dolichyl-phosphate beta-D-mannosyltransferase GDPMAN + DOLP -> GDP + dpm1 DOLMANP YAL023C 2.4.1.109 PMT2 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP + MANNAN pmt2 mannosyltransferase YDL093W 2.4.1.109 PMT5 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP + MANNAN pmt5 mannosyltransferase YDL095W 2.4.1.109 PMT1 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP + MANNAN pmt1 mannosyltransferase YGR199W 2.4.1.109 PMT6 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP + MANNAN pmt6 mannosyltransferase YJR143C 2.4.1.109 PMT4 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP + MANNAN pmt4 mannosyltransferase YOR321W 2.4.1.109 PMT3 Dolichyl-phosphate-mannose--protein DOLMANP -> DOLP + MANNAN pmt3 mannosyltransferase YBR199W 2.4.1.131 KTR4 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + ktr4 2MANPD YBR205W 2.4.1.131 KTR3 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + ktr3 2MANPD YDR483W 2.4.1.131 kre2 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + kre2 2MANPD YJL139C 2.4.1.131 yur1 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + yur1 2MANPD YKR061W 2.4.1.131 KTR2 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + ktr2 2MANPD YOR099W 2.4.1.131 KTR1 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + ktr1 2MANPD YPL053C 2.4.1.131 KTR6 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + ktr6 2MANPD Aminosugars metabolism YER062C 3.1.3.21 HOR2 DL-glycerol-3-phosphatase GL3P -> GL + PI hor2 YIL053W 3.1.3.21 RHR2 DL-glycerol-3-phosphatase GL3P -> GL + PI rhr2 YLR307W 3.5.1.41 CDA1 Chitin Deacetylase CHIT -> CHITO + AC cda1 YLR308W 3.5.1.41 CDA2 Chitin Deacetylase CHIT -> CHITO + AC cda2 Metabolism of Complex Lipids Glycerol (Glycerolipid metabolism) YFL053W 2.7.1.29 DAK2 dihydroxyacetone kinase GLYN + ATP -> T3P2 + ADP dak2 YML070W 2.7.1.29 DAK1 putative dihydroxyacetone kinase GLYN + ATP -> T3P2 + ADP dak1 YDL022W 1.1.1.8 GPD1 glycerol-3-phosphate dehydrogenase (NAD) T3P2 + NADH -> GL3P + NAD gpd1 YOL059W 1.1.1.8 GPD2 glycerol-3-phosphate dehydrogenase (NAD) T3P2 + NADH -> GL3P + NAD gpd2 YHL032C 2.7.1.30 GUT1 glycerol kinase GL + ATP -> GL3P + ADP gut1 YIL155C 1.1.99.5 GUT2 glycerol-3-phosphate dehydrogenase GL3P + FADm -> T3P2 + FADH2m gut2 DAGLY + 0.017 C100ACP + daga 0.062 C120ACP + 0.100 C140ACP + 0.270 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> TAGLY + ACP Metabolism of Cofactors, Vitamins, and Other Substances Thiamine (Vitamin B1) metabolism YOR143C 2.7.6.2 THI80 Thiamin pyrophosphokinase ATP + THIAMIN -> AMP + TPP thi80_1 YOR143C 2.7.6.2 THI80 Thiamin pyrophosphokinase ATP + TPP -> AMP + TPPP thi80_2 — thiC protein AIR -> AHM thic YOL055C 2.7.1.49 THI20 Bipartite protein consisting of N-terminal AHM + ATP -> AHMP + ADP thi20 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- terminal Pet18p-like domain of indeterminant function YPL258C 2.7.1.49 THI21 Bipartite protein consisting of N-terminal AHM + ATP -> AHMP + ADP thi21 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- terminal Pet18p-like domain of indeterminant function YPR121W 2.7.1.49 THI22 Bipartite protein consisting of N-terminal AHM + ATP -> AHMP + ADP thi22 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- terminal Pet18p-like domain of indeterminant function YOL055C 2.7.4.7 THI20 HMP-phosphate kinase AHMP + ATP -> AHMPP + ADP thid — Hypothetical T3PI + PYR -> DTP unkrxn1 — thiG protein DTP + TYR + CYS -> THZ + thig HBA + CO2 — thiE protein DTP + TYR + CYS -> THZ + thie HBA + CO2 — thiF protein DTP + TYR + CYS -> THZ + thif HBA + CO2 — thiH protein DTP + TYR + CYS -> THZ + thih HBA + CO2 YPL214C 2.7.1.50 THI6 Hydroxyethylthiazole kinase THZ + ATP -> THZP + ADP thim YPL214C 2.5.1.3 THI6 TMP pyrophosphorylase, hydroxyethylthiazole kinase THZP + AHMPP -> THMP + PPI thi6 — 2.7.4.16 Thiamin phosphate kinase THMP + ATP <-> TPP + ADP thi1 3.1.3.- (DL)-glycerol-3-phosphatase 2 THMP -> THIAMIN + PI unkrxn8 Riboflavin metabolism YBL033C 3.5.4.25 rib1 GTP cyclohydrolase II GTP -> D6RP5P + FOR + PPI rib1 YBR153W 3.5.4.26 RIB7 HTP reductase, second step in the riboflavin D6RP5P -> A6RP5P + NH3 ribd1 biosynthesis pathway YBR153W 1.1.1.193 rib7 Pyrimidine reductase A6RP5P + NADPH -> A6RP5P2 + rib7 NADP — Pyrimidine phosphatase A6RP5P2 -> A6RP + PI prm — 3,4 Dihydroxy-2-butanone-4-phosphate synthase RL5P -> DB4P + FOR ribb YBR256C 2.5.1.9 RIB5 Riboflavin biosynthesis pathway DB4P + A6RP -> D8RL + PI rib5 enzyme, 6,7-dimethyl- 8-ribityllumazine synthase, apha chain YOL143C 2.5.1.9 RIB4 Riboflavin biosynthesis pathway enzyme, 6,7-dimethyl- 8-ribityllumazine synthase, beta chain YAR071W 3.1.3.2 pho11 Acid phosphatase FMN -> RIBFLAV + PI pho11 YDR236C 2.7.1.26 FMN1 Riboflavin kinase RIBFLAV + ATP -> FMN + ADP fmn1_1 YDR236C 2.7.1.26 FMN1 Riboflavin kinase RIBFLAVm + ATPm -> FMNm + ADPm fmn1_2 YDL045C 2.7.7.2 FAD1 FAD synthetase FMN + ATP -> FAD + PPI fad1 2.7.7.2 FAD synthetase FMNm + ATPm -> FADm + PPIm fad1b Vitamin B6 (Pyridoxine) Biosynthesis metabolism — 2.7.1.35 Pyridoxine kinase PYRDX + ATP -> P5P + ADP pdxka — 2.7.1.35 Pyridoxine kinase PDLA + ATP -> PDLA5P + ADP pdxkb — 2.7.1.35 Pyridoxine kinase PL + ATP -> PL5P + ADP pdxkc YBR035C 1.4.3.5 PDX3 Pyridoxine 5′-phosphate oxidase PDLA5P + O2 -> PL5P + H2O2 + NH3 pdx3_1 YBR035C 1.4.3.5 PDX3 Pyridoxine 5′-phosphate oxidase P5P + O2 <-> PL5P + H2O2 pdx3_2 YBR035C 1.4.3.5 PDX3 Pyridoxine 5′-phosphate oxidase PYRDX + O2 <-> PL + H2O2 pdx3_3 YBR035C 1.4.3.5 PDX3 Pyridoxine 5′-phosphate oxidase PL + O2 + NH3 <-> PDLA + H2O2 pdx3_4 YBR035C 1.4.3.5 PDX3 Pyridoxine 5′-phosphate oxidase PDLA5P + O2 -> PL5P + H2O2 + NH3 pdx3_5 YOR184W 2.6.1.52 ser1 Hypothetical transaminase/phosphoserine transaminase OHB + GLU <-> PHT + AKG ser1_2 YCR053W 4.2.99.2 thr4 Threonine synthase PHT -> 4HLT + PI thr4_2 3.1.3.- Hypothetical Enzyme PDLA5P -> PDLA + PI hor2b Pantothenate and CoA biosynthesis — 3 MALCOA -> CHCOA + 2 bio1 COA + 2 CO2 — 2.3.1.47 8-Amino-7-oxononanoate synthase ALA + CHCOA <-> CO2 + biof COA + AONA YNR058W 2.6.1.62 BIO3 7,8-diamino-pelargonic acid aminotransferase (DAPA) SAM + AONA <-> SAMOB + DANNA bio3 aminotransferase YNR057C 6.3.3.3 BIO4 dethiobiotin synthetase CO2 + DANNA + ATP <-> DTB + bio4 PI + ADP YGR286C 2.8.1.6 BIO2 Biotin synthase DTB + CYS <-> BT bio2 Folate biosynthesis YGR267C 3.5.4.16 fol2 GTP cyclohydrolase I GTP -> FOR + AHTD fol2 — 3.6.1.- Dihydroneopterin triphosphate pyrophosphorylase AHTD -> PPI + DHPP ntpa YDR481C 3.1.3.1 pho8 Glycerophosphatase, Alkaline phosphatase, Nucleoside AHTD -> DHP + 3 PI pho8 triphosphatase YDL100C 3.6.1.- YDL100C Dihydroneopterin monophosphate dephosphorylase DHPP -> DHP + PI dhdnpa YNL256W 4.1.2.25 fol1 Dihydroneopterin aldolase DHP -> AHHMP + fol1_1 GLAL YNL256W 2.7.6.3 fol1 6-Hydroxymethyl-7,8 AHHMP + ATP -> AMP + AHHMD fol1_2 dihydropterin pyrophosphokinase YNR033W 4.1.3.- ABZ1 Aminodeoxychorismate synthase CHOR + GLN -> ADCHOR + GLU abz1 — 4.--.- Aminodeoxychorismate lyase ADCHOR -> PYR + PABA pabc YNL256W 2.5.1.15 fol1 Dihydropteroate synthase PABA + AHHMD -> PPI + DHPT fol1_3 YNL256W 2.5.1.15 fol1 Dihydropteroate synthase PABA + AHHMP -> DHPT fol1_4 — 6.3.2.12 Dihydrofolate synthase DHPT + ATP + GLU -> ADP + folc PI + DHF YOR236W 1.5.1.3 dfr1 Dihydrofolate reductase DHFm + NADPHm -> NADPm + dfr1_1 THFm YOR236W 1.5.1.3 dfr1 Dihydrofolate reductase DHF + NADPH -> NADP + THF dfr1_2 — 6.3.3.2 5-Formyltetrahydrofolate cyclo-ligase ATPm + FTHFm -> ADPm + ftfa PIm + MTHFm — 6.3.3.2 5-Formyltetrahydrofolate cyclo-ligase ATP + FTHF -> ADP + ftfb PI + MTHF YKL132C 6.3.2.17 RMA1 Protein with similarity to folylpolyglutamate synthase; THF + ATP + GLU <-> ADP + rma1 converts tetrahydrofolyl-[Glu(n)] + glutamate to PI + THFG tetrahydrofolyl-[Glu(n + 1)] YMR113W 6.3.2.17 FOL3 Dihydrofolate synthetase THF + ATP + GLU <-> ADP + fol3 PI + THFG YOR241W 6.3.2.17 MET7 Folylpolyglutamate synthetase, involved in methionine THF + ATP + GLU <-> ADP + met7 biosynthesis and maintenance PI + THFG of mitochondrial genome One carbon pool by folate |MAP: 00670| YPL023C 1.5.1.20 MET12 Methylene tetrahydrofolate reductase METTHFm + NADPHm -> NADPm + met12 MTHFm YGL125W 1.5.1.20 met13 Methylene tetrahydrofolate reductase METTHFm + NADPHm -> NADPm + met13 MTHFm YBR084W 1.5.1.5 mis1 the mitochondrial trifunctional enzyme C1- METTHFm + NADPm <-> METHFm + mis1_1 tetrahydroflate synthase NADPHm YGR204W 1.5.1.5 ade3 the cytoplasmic trifunctional enzyme C1- METTHF + NADP <-> METHF + ade3_1 tetrahydrofolate synthase NADPH YBR084W 6.3.4.3 mis1 the mitochondrial trifunctional enzyme C1- THFm + FORm + ATPm -> ADPm + mis1_2 tetrahydroflate synthase PIm + FTHFm YGR204W 6.3.4.3 ade3 the cytoplasmic trifunctional enzyme C1- THF + FOR + ATP -> ADP + ade3_2 tetrahydrofolate synthase PI + FTHF YBR084W 3.5.4.9 mis1 the mitochondrial trifunctional enzyme C1- METHFm <-> FTHFm mis1_3 tetrahydroflate synthase YGR204W 3.5.4.9 ade3 the cytoplasmic trifunctional enzyme C1- METHF <-> FTHF ade3_3 tetrahydrofolate synthase YKR080W 1.5.1.15 MTD1 NAD-dependent 5,10-methylenetetrahydrafolate METTHF + NAD -> METHF + NADH mtd1 dehydrogenase YBL013W 2.1.2.9 fmt1 Methionyl-tRNA Transformylase FTHFm + MTRNAm -> THFm + fmt1 FMRNAm Coenzyme A Biosynthesis YBR176W 2.1.2.11 ECM31 Ketopentoate hydroxymethyl transferase OIVAL + METTHF -> AKP + THF ecm31 YHR063C 1.1.1.169 PAN5 Putative ketopantoate reductase (2-dehydropantoate 2- AKP + NADPH -> NADP + PANT pane reductase) involved in coenzyme A synthesis, has similarity to Cbs2p, Ketopantoate reductase YLR355C 1.1.1.86 ilv5 Ketol-acid reductoisomerase AKPm + NADPHm -> NADPm + ilv5_3 PANTm YIL145C 6.3.2.1 YIL145C Pantoate-b-alanine ligase PANT + bALA + ATP -> AMP + panca PPI + PNTO YDR531W 2.7.1.33 YDR531W Putative pantothenate kinase involved in coenzyme A PNTO + ATP -> ADP + 4PPNTO coaa biosynthesis, Pantothenate kinase — 6.3.2.5 Phosphopantothenate-cysteine ligase 4PPNTO + CTP + CYS -> CMP + pchg PPI + 4PPNCYS — 4.1.1.36 Phosphopantothenate-cysteine decarboxylase 4PPNCYS -> CO2 + 4PPNTE pcdcl — 2.7.7.3 Phospho-pantethiene adenylyltransferase 4PPNTE + ATP -> PPI + DPCOA patrana — 2.7.7.3 Phospho-pantethiene adenylyltransferase 4PPNTEm + ATPm -> PPIm + DPCOAm patranb — 2.7.1.24 DephosphoCoA kinase DPCOA + ATP -> ADP + COA dphcoaka — 2.7.1.24 DephosphoCoA kinase DPCOAm + ATPm -> ADPm + COAm dphcoakb — 4.1.1.11 ASPARTATE ALPHA-DECARBOXYLASE ASP -> CO2 + bALA pancb YPL148C 2.7.8.7 PPT2 Acyl carrier-protein synthase, phosphopantetheine COA -> PAP + ACP acps protein transferase for Acp1p NAD Biosynthesis YGL037C 3.5.1.19 PNC1 Nicotinamidase NAM <-> NAC + NH3 nadh YOR209C 2.4.2.11 NPT1 NAPRTase NAC + PRPP -> NAMN + PPI npt1 1.4.3.- Aspartate oxidase ASP + FADm -> FADH2m + ISUCC nadb 1.4.3.16 Quinolate synthase ISUCC + T3P2 -> PI + QA nada YFR047C 2.4.2.19 QPT1 Quinolate phosphoribosyl transferase QA + PRPP -> NAMN + CO2 + PPI nadc YLR328W 2.7.7.18 YLR328W Nicotinamide mononucleotide (NMN) NAMN + ATP -> PPI + NAAD nadd1 adenylyltransferase YHR074W 6.3.5.1 QNS1 Deamido-NAD ammonia ligase NAAD + ATP + NH3 -> NAD + nade AMP + PPI YJR049c 2.7.1.23 utr1 NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP + nadf_1 2.7.4.1)/NAD + KINASE (EC 2.7.1.23) ADP YEL041w 2.7.1.23 YEL041w NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP + ADP nadf_2 2.7.4.1)/NAD + KINASE (EC 2.7.1.23) YPL188w 2.7.1.23 POS5 NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP + ADP nadf_5 2.7.4.1)/NAD + KINASE (EC 2.7.1.23) 3.1.2.- NADP phosphatase NADP -> NAD + PI nadphps 3.2.2.5 NAD -> NAM + ADPRIB nad1 2.4.2.1 strong similarity to purine-nucleoside phosphorylases ADN + PI <-> AD + RIP nadg1 2.4.2.1 strong similarity to purine-nucleoside phosphorylases GSN + PI <-> GN + RIP nadg2 Nicotinic Acid synthesis from TRP YFR047C 2.4.2.19 QPT1 Quinolate phosphoribosyl transferase QAm + PRPPm -> NAMNm + mnadc CO2m + PPIm YLR328W 2.7.7.18 YLR328W NAMN adenylyl transferase NAMNm + ATPm -> PPIm + NAADm mnadd1 YLR328W 2.7.7.18 YLR328W NAMN adenylyl transferase NMNm + ATPm -> NADm + mnadd2 PPIm YHR074W 6.3.5.1 QNS1 Deamido-NAD ammonia ligase NAADm + ATPm + NH3m -> NADm + mnade AMPm + PPIm YJR049c 2.7.1.23 utr1 NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm -> NADPm + ADPm mnadf_1 2.7.4.1)/NAD + KINASE (EC 2.7.1.23) YPL188w 2.7.1.23 POS5 NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm -> NADPm + ADPm mnadf_2 2.7.4.1)/NAD + KINASE (EC 2.7.1.23) YEL041w 2.7.1.23 YEL041w NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm -> NADPm + ADPm mnadf_5 2.7.4.1)/NAD + KINASE (EC 2.7.1.23) — 3.1.2.- NADP phosphatase NADPm -> NADm + PIm mnadphps YLR209C 2.4.2.1 PNP1 strong similarity to purine-nucleoside phosphorylases ADNm + PIm <-> ADm + RIPm mnadg1 YLR209C 2.4.2.1 PNP1 strong similarity to purine-nucleoside phosphorylases GSNm + PIm <-> GNm + RIPm mnadg2 YGL037C 3.5.1.19 PNC1 Nicotinamidase NAMm <-> NACm + NH3m mnadh YOR209C 2.4.2.11 NPT1 NAPRTase NACm + PRPPm -> NAMNm + PPIm mnpt1 3.2.2.5 NADm -> NAMm + ADPRIBm mnad1 Uptake Pathways Porphyrin and Chlorophyll Metabolism YDR232W 2.3.1.37 hem1 5-Aminolevulinate synthase SUCCOAm + GLYm -> ALAVm + hem1 COAm + CO2m YGL040C 4.2.1.24 HEM2 Aminolevulinate dehydratase 2 ALAV -> PBG hem2 YDL205C 4.3.1.8 HEM3 Hydroxymethylbilane synthase 4 PBG -> HMB + 4 NH3 hem3 YOR278W 4.2.1.75 HEM4 Uroporphyrinogen-III synthase HMB -> UPRG hem4 YDR047W 4.1.1.37 HEM12 Uroporphyrinogen decarboxylase UPRG -> 4 CO2 + CPP hem12 YDR044W 1.3.3.3 HEM13 Coproporphyrinogen oxidase, aerobic O2 + CPP -> 2 CO2 + PPHG hem13 YER014W 1.3.3.4 HEM14 Protoporphyrinogen oxidase O2 + PPHGm -> PPIXm hem14 YOR176W 4.99.1.1 HEM15 Ferrochelatase PPIXm -> PTHm hem15 YGL245W 6.1.1.17 YGL245W glutamyl-tRNA synthetase, cytoplasmic GLU + ATP -> GTRNA + unrxn10 AMP + PPI YOL033W 6.1.1.17 MSE1 GLUm + ATPm -> GTRNAm + mse1 AMPm + PPIm YKR069W 2.1.1.107 met1 uroporphyrin-III C-methyltransferase SAM + UPRG -> SAH + PC2 met1 Quinone Biosynthesis YKL211C 4.1.3.27 trp3 anthranilate synthase Component II and indole-3- CHOR -> 4HBZ + PYR trp3_3 phosphate (multifunctional enzyme) YER090W 4.1.3.27 trp2 anthranilate synthase Component I CHOR -> 4HBZ + PYR trp2_2 YPR176C 2.5.1.- BET2 geranylgeranyltransferase type II beta subunit 4HBZ + NPP -> N4HBZ + PPI bet2 YJL031C 2.5.1.- BET4 geranylgeranyltransferase type II alpha subunit YGL155W 2.5.1.- cdc43 geranylgeranyltransferase type I beta subunit YBR003W 2.5.1.- COQ1 Hexaprenyl pyrophosphate synthetase, catalyzes the 4HBZ + NPP -> N4HBZ + PPI coq1 first step in coenzyme Q (ubiquinone) biosynthesis pathway YNR041C 2.5.1.- COQ2 para-hydroxybenzoate--polyprenyltransferase 4HBZ + NPP -> N4HBZ + PPI coq2 YPL172C 2.5.1.- COX10 protoheme IX farnesyltransferase, mitochondrial 4HBZ + NPP -> N4HBZ + PPI cox10 precursor YDL090C 2.5.1.- ram1 protein farnesyltransferase beta subunit 4HBZ + NPP -> N4HBZ + PPI ram1 YKL019W 2.5.1.- RAM2 protein farnesyltransferase alpha subunit YBR002C 2.5.1.- RER2 putative dehydrodolichyl diphospate synthetase 4HBZ + NPP -> N4HBZ + PPI rer2 YMR101C 2.5.1.- SRT1 putative dehydrodolichyl diphospate synthetase 4HBZ + NPP -> N4HBZ + PPI srt1 YDR538W 4.1.1.- PAD1 Octaprenyl-hydroxybenzoate decarboxylase N4HBZ -> CO2 + 2NPPP pad1_2 — 1.13.14.- 2-Octaprenylphenol hydroxylase 2NPPP + O2 -> 2N6H ubib YPL266W 2.1.1.- DIM1 2N6H + SAM -> 2NPMP + dim1 SAH — 1.14.13.- 2-Octaprenyl-6-methoxyphenol hydroxylase 2NPMPm + ubih O2m -> 2NPMBm YML110C 2.1.1.- COQ5 2-Octaprenyl-6-methoxy-1,4-benzoquinone methylase 2NPMBm + SAMm -> 2NPMMBm + coq5 SAHm YGR255C 1.14.13- COQ6 COQ6 monooxygenase 2NPMMBm + coq6b O2m -> 2NMHMBm YOL096C 2.1.1.64 COQ3 3-Dimethylubiquinone 3-methyltransferase 2NMHMBm + SAMm -> QH2m + ubig SAHm Memberane Transport Mitochondiral Membrane Transport The followings diffuse through the inner mitochondiral membrane in a non-carrier-mediated manner: O2 <-> O2m mo2 CO2 <-> CO2m mco2 ETH <-> ETHm meth NH3 <-> NH3m mnh3 MTHN <-> MTHNm mmthn THFm <-> THF mthf METTHFm <-> METTHF mmthf SERm <-> SER mser GLYm <-> GLY mgly CBHCAPm <-> CBHCAP mcbh OICAPm <-> OICAP moicap PROm <-> PRO mpro CMPm <-> CMP mcmp ACm <-> AC mac ACAR -> ACARm macar_(—) CARm -> CAR mcar_(—) ACLAC <-> ACLACm maclac ACTAC <-> ACTACm mactc SLF -> SLFm + Hm mslf THRm <-> THR mthr AKAm -> AKA maka YMR056c AAC1 ADP/ATP carrier protein (MCF) ADP + ATPm + PI -> Hm + ADPm + aac1 ATP + PIm YBL030C pet9 ADP/ATP carrier protein (MCF) ADP + ATPm + PI -> Hm + ADPm + pet9 ATP + PIm YBR085w AAC3 ADP/ATP carrier protein (MCF) ADP + ATPm + PI -> Hm + ADPm + aac3 ATP + PIm YJR077C MIR1 phosphate carrier PI <-> Hm + PIm mir1a YER053C YER053C similarity to C. elegans mitochondrial PI + OHm <-> PIm mir1d phosphate carrier YLR348C DIC1 dicarboxylate carrier MAL + SUCCm <-> MALm + SUCC dic1_1 YLR348C DIC1 dicarboxylate carrier MAL + PIm <-> MALm + PI dic1_2 YLR348C DIC1 dicarboxylate carrier SUCC + PIm -> SUCCm + PI dic1_3 MALT + PIm <-> MALTm + PI mmlt YKL120W OAC1 Mitochondrial oxaloacetate carrier OA <-> OAm + Hm moab YBR291C CTP1 citrate transport protein CIT + MALm <-> CITm + MAL ctp1_1 YBR291C CTP1 citrate transport protein CIT + PEPm <-> CITm + PEP ctp1_2 YBR291C CTP1 citrate transport protein CIT + ICITm <-> CITm + ICIT ctp1_3 IPPMAL <-> IPPMALm mpma1R LAC <-> LACm + Hm mlac pyruvate carrier PYR <-> PYRm + Hm pyrca glutamate carrier GLU <-> GLUm + Hm gca GLU + OHm -> GLUm gcb YOR130C ORT1 ornithine carrier ORN + Hm <-> ORNm ort1 YOR100C CRC1 carnitine carrier CARm + ACAR -> CAR + ACARm crc1 OIVAL <-> OIVALm moival OMVAL <-> OMVALm momval YIL134W FLX1 Protein involved in transport of FAD from cytosol into FAD + FMNm -> FADm + FMN mfad the mitochondrial matrix RIBFLAV <-> RIBFLAVm mribo DTB <-> DTBm mdtb H3MCOA <-> H3MCOAm mmcoa MVL <-> MVLm mmv1 PA <-> PAm mpa 4PPNTE <-> 4PPNTEm mppnt AD <-> ADm mad PRPP <-> PRPPm mprpp DHF <-> DHFm mdhf QA <-> QAm mqa OPP <-> OPPm mopp SAM <-> SAMm msam SAH <-> SAHm msah YJR095W SFC1 Mitochondrial membrane succinate-fumarate SUCC + FUMm -> SUCCm + FUM sfc1 transporter, member of the mitochondrial carrier family (MCF) of membrane transporters YPL134C ODC1 2-oxodicarboylate transporter AKGm + OXA <-> AKG + OXAm odc1 YOR222W ODC2 2-oxodicarboylate transporter AKGm + OXA <-> AKG + OXAm odc2 Malate Aspartate Shuttle Included elsewhere Glycerol phosphate shuttle T3P2m -> T3P2 mt3p GL3P -> GL3Pm mgl3p Plasma Membrane Transport Carbohydrates YHR092c HXT4 moderate- to low-affinity glucose transporter GLCxt -> GLC hxt4 YLR081w GAL2 galactose (and glucose) permease GLCxt -> GLC gal2_3 YOL156w HXT11 low affinity glucose transport protein GLCxt -> GLC hxt11 YDR536W stl1 Protein member of the hexose transporter family GLCxt -> GLC stl1_1 YHR094c hxt1 High-affinity hexose (glucose) transporter GLCxt -> GLC hxt1_1 YOL156w HXT11 Glucose permease GLCxt -> GLC hxt11_1 YEL069c HXT13 high-affinity hexose transporter GLCxt -> GLC hxt13_1 YDL245c HXT15 Hexose transporter GLCxt -> GLC hxt15_1 YJR158w HXT16 hexose permease GLCxt -> GLC hxt16_1 YFL011w HXT10 high-affinity hexose transporter GLCxt -> GLC hxt10_1 YNR072w HXT17 Putative hexose transporter GLCxt -> GLC hxt17_1 YMR011w HXT2 high affinity hexose transporter-2 GLCxt -> GLC hxt2_1 YHR092c hxt4 High-affinity glucose transporter GLCxt -> GLC hxt4_1 YDR345c hxt3 Low-affinity glucose transporter GLCxt -> GLC hxt3_1 YHR096c HXT5 hexose transporter GLCxt -> GLC hxt5_1 YDR343c HXT6 Hexose transporter GLCxt -> GLC hxt6_1 YDR342c HXT7 Hexose transporter GLCxt -> GLC hxt7_1 YJL214w HXT8 hexose permease GLCxt -> GLC hxt8_4 YJL219w HXT9 hexose permease GLCxt -> GLC hxt9_1 YLR081w gal2 galactose permease GLACxt + HEXT -> GLAC gal2_1 YFL011w HXT10 high-affinity hexose transporter GLACxt + HEXT -> GLAC hxt10_4 YOL156w HXT11 Glucose permease GLACxt + HEXT -> GLAC hxt11_4 YNL318c HXT14 Member of the hexose transporter family GLACxt + HEXT -> GLAC hxt14 YJL219w HXT9 hexose permease GLACxt + HEXT -> GLAC hxt9_4 YDR536W stl1 Protein member of the hexose transporter family GLACxt + HEXT -> GLAC stl1_4 YFL055w AGP3 Amino acid permease for serine, aspartate, and GLUxt + HEXT <-> GLU agp3_3 glutamate YDR536W stl1 Protein member of the hexose transporter family GLUxt + HEXT <-> GLU stl1_2 YKR039W gap1 General amino acid permease GLUxt + HEXT <-> GLU gap8 YCL025C AGP1 Amino acid permease for most neutral amino acids GLUxt + HEXT <-> GLU gap24 YPL265W DIP5 Dicarboxylic amino acid permease GLUxt + HEXT <-> GLU dip10 YDR536W stl1 Protein member of the hexose transporter family GLUxt + HEXT <-> GLU stl1_3 YHR094c hxt1 High-affinity hexose (glucose) transporter FRUxt + HEXT -> FRU hxt1_2 YFL011w HXT10 high-affinity hexose transporter FRUxt + HEXT -> FRU hxt10_2 YOL156w HXT11 Glucose permease FRUxt + HEXT -> FRU hxt11_2 YEL069c HXT13 high-affinity hexose transporter FRUxt + HEXT -> FRU hxt13_2 YDL245c HXT15 Hexose transporter FRUxt + HEXT -> FRU hxt15_2 YJR158w HXT16 hexose permease FRUxt + HEXT -> FRU hxt16_2 YNR072w HXT17 Putative hexose transporter FRUxt + HEXT -> FRU hxt17_2 YMR011w HXT2 high affinity hexose transporter-2 FRUxt + HEXT -> FRU hxt2_2 YDR345c hxt3 Low-affinity glucose transporter FRUxt + HEXT -> FRU hxt3_2 YHR092c hxt4 High-affinity glucose transporter FRUxt + HEXT -> FRU hxt4_2 YHR096c HXT5 hexose transporter FRUxt + HEXT -> FRU hxt5_2 YDR343c HXT6 Hexose transporter FRUxt + HEXT -> FRU hxt6_2 YDR342c HXT7 Hexose transporter FRUxt + HEXT -> FRU hxt7_2 YJL214w HXT8 hexose permease FRUxt + HEXT -> FRU hxt8_5 YJL219w HXT9 hexose permease FRUxt + HEXT -> FRU hxt9_2 YHR094c hxt1 High-affinity hexose (glucose) transporter MANxt + HEXT -> MAN hxt1_5 YFL011w HXT10 high-affinity hexose transporter MANxt + HEXT -> MAN hxt10_3 YOL156w HXT11 Glucose permease MANxt + HEXT -> MAN hxt11_3 YEL069c HXT13 high-affinity hexose transporter MANxt + HEXT -> MAN hxt13_3 YDL245c HXT15 Hexose transporter MANxt + HEXT -> MAN hxt15_3 YJR158w HXT16 hexose permease MANxt + HEXT -> MAN hxt16_3 YNR072w HXT17 Putative hexose transporter MANxt + HEXT -> MAN hxt17_3 YMR011w HXT2 high affinity hexose transporter-2 MANxt + HEXT -> MAN hxt2_3 YDR345c hxt3 Low-affinity glucose transporter MANxt + HEXT -> MAN hxt3_3 YHR092c hxt4 High-affinity glucose transporter MANxt + HEXT -> MAN hxt4_3 YHR096c HXT5 hexose transporter MANxt + HEXT -> MAN hxt5_3 YDR343c HXT6 Hexose transporter MANxt + HEXT -> MAN hxt6_3 YDR342c HXT7 Hexose transporter MANxt + HEXT -> MAN hxt7_3 YJL214w HXT8 hexose permease MANxt + HEXT -> MAN hxt8_6 YJL219w HXT9 hexose permease MANxt + HEXT -> MAN hxt9_3 YDR497c ITR1 myo-inositol transporter MIxt + HEXT -> MI itr1 YOL103w ITR2 myo-inositol transporter MIxt + HEXT -> MI itr2 Maltase permease MLTxt + HEXT -> MLT mltup YIL162W 3.2.1.26 SUC2 invertase (sucrose hydrolyzing enzyme) SUCxt -> GLCxt + FRUxt suc2 sucrose SUCxt + HEXT -> SUC sucup YBR298c MAL31 Dicarboxylates MALxt + HEXT <-> MAL mal31 a-Ketoglutarate/malate translocator MALxt + AKG <-> MAL + AKGxt akmup a-methylglucoside AMGxt <-> AMG amgup Sorbose SORxt <-> SOR sorup Arabinose (low affinity) ARABxt <-> ARAB arbup1 Fucose FUCxt + HEXT <-> FUC fucup GLTLxt + HEXT -> GLTL gltlupb Glucitol GLTxt + HEXT -> GLT gltup Glucosamine GLAMxt + HEXT <-> GLAM gaup YLL043W FPS1 Glycerol GLxt <-> GL glup YKL217W JEN1 Lactate transport LACxt + HEXT <-> LAC lacup1 Mannitol MNTxt + HEXT -> MNT mntup Melibiose MELIxt + HEXT -> MELI melup_1 N-Acetylglucosamine NAGxt + HEXT -> NAG nagup Rhamnose RMNxt + HEXT -> RMN rmnup Ribose RIBxt + HEXT -> RIB ribup Trehalose TRExt + HEXT -> TRE treup_1 TRExt -> AATRE6P treup_2 XYLxt <-> XYL xylup Amino Acids YKR039W gap1 General amino acid permease ALAxt + HEXT <-> ALA gap1_1 YPL265W DIP5 Dicarboxylic amino acid permease ALAxt + HEXT <-> ALA dip5 YCL025C AGP1 Amino acid permease for most neutral amino acids ALAxt + HEXT <-> ALA gap25 YOL020W TAT2 Tryptophan permease ALAxt + HEXT <-> ALA tat5 YOR348C PUT4 Proline permease ALAxt + HEXT <-> ALA put4 YKR039W gap1 General amino acid permease ARGxt + HEXT <-> ARG gap2 YEL063C can1 Permease for basic amino acids ARGxt + HEXT <-> ARG can1_1 YNL270C ALP1 Protein with strong similarity to permeases ARGxt + HEXT <-> ARG alp1 YKR039W gap1 General amino acid permease ASNxt + HEXT <-> ASN gap3 YCL025C AGP1 Amino acid permease for most neutral amino acids ASNxt + HEXT <-> ASN gap21 YDR508C GNP1 Glutamine permease (high affinity) ASNxt + HEXT <-> ASN gnp2 YPL265W DIP5 Dicarboxylic amino acid permease ASNxt + HEXT <-> ASN dip6 YFL055W AGP3 Amino acid permease for serine, aspartate, and ASPxt + HEXT <-> ASP agp3_2 glutamate YKR039W gap1 General amino acid permease ASPxt + HEXT <-> ASP gap4 YPL265W DIP5 Dicarboxylic amino acid permease ASPxt + HEXT <-> ASP dip7 YKR039W gap1 General amino acid permease CYSxt + HEXT <-> CYS gap5 YDR508C GNP1 Glutamine permease (high affinity) CYSxt + HEXT <-> CYS gnp3 YBR068C BAP2 Branched chain amino acid permease CYSxt + HEXT <-> CYS bap2_1 YDR046C BAP3 Branched chain amino acid permease CYSxt + HEXT <-> CYS bap3_1 YBR069C VAP1 Amino acid permease CYSxt + HEXT <-> CYS vap7 YOL020W TAT2 Tryptophan permease CYSxt + HEXT <-> CYS tat7 YKR039W gap1 General amino acid permease GLYxt + HEXT <-> GLY gap6 YOL020W TAT2 Tryptophan permease GLYxt + HEXT <-> GLY tat6 YPL265W DIP5 Dicarboxylic amino acid permease GLYxt + HEXT <-> GLY dip8 YOR348C PUT4 Proline permease GLYxt + HEXT <-> GLY put5 YKR039W gap1 General amino acid permease GLNxt + HEXT <-> GLN gap7 YCL025C AGP1 Amino acid permease for most neutral amino acids GLNxt + HEXT <-> GLN gap22 YDR508C GNP1 Glutamine permease (high affinity) GLNxt + HEXT <-> GLN gnp1 YPL265W DIP5 Dicarboxylic amino acid permease GLNxt + HEXT <-> GLN dip9 YGR191W HIP1 Histidine permease HISxt + HEXT <-> HIS hip1 YKR039W gap1 General amino acid permease HISxt + HEXT <-> HIS gap9 YCL025C AGP1 Amino acid permease for most neutral amino acids HISxt + HEXT <-> HIS gap23 YBR069C VAP1 Amino acid permease HISxt + HEXT <-> HIS vap6 YBR069C TAT1 Amino acid permease that transports valine, leucine, ILExt + HEXT <-> ILE tat1_2 isleucine, tyrosine, tryptophan, and threonine YKR039W gap1 General amino acid permease ILExt + HEXT <-> ILE gap10 YCL025C AGP1 Amino acid permease for most neutral amino acids ILExt + HEXT <-> ILE gap32 YBR068C BAP2 Branched chain amino acid permease ILExt + HEXT <-> ILE bap2_2 YDR046C BAP3 Branched chain amino acid permease ILExt + HEXT <-> ILE bap3_2 YBR069C VAP1 Amino acid permease ILExt + HEXT <-> ILE vap3 YBR069C TAT1 Amino acid permease that transports valine, leucine, LEUxt + HEXT <-> LEU tat1_3 isleucine, tyrosine, tryptophan, and threonine YKR039W gap1 General amino acid permease LEUxt + HEXT <-> LEU gap11 YCL025C AGP1 Amino acid permease for most neutral amino acids LEUxt + HEXT <-> LEU gap33 YBR068C BAP2 Branched chain amino acid permease LEUxt + HEXT <-> LEU bap2_3 YDR046C BAP3 Branched chain amino acid permease LEUxt + HEXT <-> LEU bap3_3 YBR069C VAP1 Amino acid permease LEUxt + HEXT <-> LEU vap4 YDR508C GNP1 Glutamine permease (high affinity) LEUxt + HEXT <-> LEU gnp7 YKR039W gap1 General amino acid permease METxt + HEXT <-> MET gap13 YCL025C AGP1 Amino acid permease for most neutral amino acids METxt + HEXT <-> MET gap26 YDR508C GNP1 Glutamine permease (high affinity) METxt + HEXT <-> MET gnp4 YBR068C BAP2 Branched chain amino acid permease METxt + HEXT <-> MET bap2_4 YDR046C BAP3 Branched chain amino acid permease METxt + HEXT <-> MET bap3_4 YGR055W MUP1 High-affinity methionine permease METxt + HEXT <-> MET mup1 YHL036W MUP3 Low-affinity methionine permease METxt + HEXT <-> MET mup3 YKR039W gap1 General amino acid permease PHExt + HEXT <-> PHEN gap14 YCL025C AGP1 Amino acid permease for most neutral amino acids PHExt + HEXT <-> PHEN gap29 YOL020W TAT2 Tryptophan permease PHExt + HEXT <-> PHEN tat4 YBR068C BAP2 Branched chain amino acid permease PHExt + HEXT <-> PHEN bap2_5 YDR046C BAP3 Branched chain amino acid permease PHExt + HEXT <-> PHEN bap3_5 YKR039W gap1 General amino acid permease PROxt + HEXT <-> PRO gap15 YOR348C PUT4 Proline permease PROxt + HEXT <-> PRO put6 YBR069C TAT1 Amino acid permease that transports valine, leucine, TRPxt + HEXT <-> TRP tat1_6 isleucine, tyrosine, tryptophan, and threonine YKR039W gap1 General amino acid permease TRPxt + HEXT <-> TRP gap18 YBR069C VAP1 Amino acid permease TRPxt + HEXT <-> TRP vap2 YOL020W TAT2 Tryptophan permease TRPxt + HEXT <-> TRP tat3 YBR068C BAP2 Branched chain amino acid permease TRPxt + HEXT <-> TRP bap2_6 YDR046C BAP3 Branched chain amino acid permease TRPxt + HEXT <-> TRP bap3_6 YBR069C TAT1 Amino acid permease that transports valine, leucine, TYRxt + HEXT <-> TYR tat1_7 isleucine, tyrosine, tryptophan, and threonine YKR039W gap1 General amino acid permease TYRxt + HEXT <-> TYR gap19 YCL025C AGP1 Amino acid permease for most neutral amino acids TYRxt + HEXT <-> TYR gap28 YBR068C BAP2 Branched chain amino acid permease TYRxt + HEXT <-> TYR bap2_7 YBR069C VAP1 Amino acid permease TYRxt + HEXT <-> TYR vap1 YOL020W TAT2 Tryptophan permease TYRxt + HEXT <-> TYR tat2 YDR046C BAP3 Branched chain amino acid permease TYRxt + HEXT <-> TYR bap3_7 YKR039W gap1 General amino acid permease VALxt + HEXT <-> VAL gap20 YCL025C AGP1 Amino acid permease for most neutral amino acids VALxt + HEXT <-> VAL gap31 YDR046C BAP3 Branched chain amino acid permease VALxt + HEXT <-> VAL bap3_8 YBR069C VAP1 Amino acid permease VALxt + HEXT <-> VAL vap5 YBR068C BAP2 Branched chain amino acid permease VALxt + HEXT <-> VAL bap2_8 YFL055W AGP3 Amino acid permease for serine, aspartate, and SERxt + HEXT <-> SER agp3_1 glutamate YCL025C AGP1 Amino acid permease for most neutral amino acids SERxt + HEXT <-> SER gap27 YDR508C GNP1 Glutamine permease (high affinity) SERxt + HEXT <-> SER gnp5 YKR039W gap1 General amino acid permease SERxt + HEXT <-> SER gap16 YPL265W DIP5 Dicarboxylic amino acid permease SERxt + HEXT <-> SER dip11 YBR069C TAT1 Amino acid permease that transports valine, leucine, THRxt + HEXT <-> THR tat1_1 isleucine, tyrosine, tryptophan, and threonine YCL025C AGP1 Amino acid permease for most neutral amino acids THRxt + HEXT <-> THR gap30 YKR039W gap1 General amino acid permease THRxt + HEXT <-> THR gap17 YDR508C GNP1 Glutamine permease (high affinity) THRxt + HEXT <-> THR gnp6 YNL268W LYP1 Lysine specific permease (high affinity) LYSxt + HEXT <-> LYS lyp1 YKR039W gap1 General amino acid permease LYSxt + HEXT <-> LYS gap12 YLL061W MMP1 High affinity S-methylmethionine permease MMETxt + HEXT -> MMET mmp1 YPL274W SAM3 High affinity S-adenosylmethionine permease SAMxt + HEXT -> SAM sam3 YOR348C PUT4 Proline permease GABAxt + HEXT -> GABA put7 YDL210W uga4 Amino acid permease with high specificity for GABA GABAxt + HEXT -> GABA uga4 YBR132C AGP2 Plasma membrane carnitine transporter CARxt <-> CAR agp2 YGL077C HNM1 Choline permease CHOxt + HEXT -> MET hnml YNR056C BIO5 transmembrane regulator of KAPA/DAPA transport BIOxt + HEXT -> BIO bio5a YDL210W uga4 Amino acid permease with high specificity for GABA ALAVxt + HEXT -> ALAV uga5 YKR039W gap1 General amino acid permease ORNxt + HEXT <-> ORN gap1b YEL063C can1 Permease for basic amino acids ORNxt + HEXT <-> ORN can1b Putrescine PTRSCxt + HEXT -> PTRSC ptrup Spermidine & putrescine SPRMDxt + HEXT -> SPRMD sprup1 YKR093W PTR2 Dipeptide DIPEPxt + HEXT -> DIPEP ptr2 YKR093W PTR2 Oligopeptide OPEPxt + HEXT -> OPEP ptr3 YKR093W PTR2 Peptide PEPTxt + HEXT -> PEPT ptr4 YBR021W FUR4 Uracil URAxt + HEXT -> URA uraup1 Nicotinamide mononucleotide transporter NMNxt + HEXT -> NMN nmnup YER056C FCY2 Cytosine purine permease CYTSxt + HEXT -> CYTS fcy2_1 YER056C FCY2 Adenine ADxt + HEXT -> AD fcy2_2 YER056C FCY2 Guanine GNxt + HEXT <-> GN fcy2_3 YER060W FCY21 Cytosine purine permease CYTSxt + HEXT -> CYTS fcy21_1 YER060W FCY21 Adenine ADxt + HEXT -> AD fcy21_2 YER060W FCY21 Guanine GNxt + HEXT <-> GN fcy21_3 YER060W- FCY22 Cytosine purine permease CYTSxt + HEXT -> CYTS fcy22_1 A YER060W- FCY22 Adenine ADxt + HEXT -> AD fcy22_2 A YER060W- FCY22 Guanine GNxt + HEXT <-> GN fcy22_3 A YGL186C YGL186C Cytosine purine permease CYTSxt + HEXT -> CYTS cytup1 YGL186C YGL186C Adenine ADxt + HEXT -> AD adup1 YGL186C YGL186C Guanine GNxt + HEXT <-> GN gnup G-system ADNxt + HEXT -> ADN ncgup1 G-system GSNxt + HEXT -> GSN ncgup3 YBL042C FUI1 Uridine permease, G-system URIxt + HEXT -> URI uriup G-system CYTDxt + HEXT -> CYTD ncgup4 G-system (transports all nucleosides) INSxt + HEXT -> INS ncgup5 G-system XTSINExt + HEXT -> XTSINE ncgup6 G-system DTxt + HEXT -> DT ncgup7 G-system DINxt + HEXT -> DIN ncgup8 G-system DGxt + HEXT -> DG ncgup9 G-system DAxt + HEXT -> DA ncgup10 G-system DCxt + HEXT -> DC ncgup11 G-system DUxt + HEXT -> DU ncgup12 C-system ADNxt + HEXT -> ADN nccup1 YBL042C FUI1 Uridine permease, C-system URIxt + HEXT -> URI nccup2 C-system CYTDxt + HEXT -> CYTD nccup3 C-system DTxt + HEXT -> DT nccup4 C-system DAxt + HEXT -> DA nccup5 C-system DCxt + HEXT -> DC nccup6 C-system DUxt + HEXT -> DU nccup7 Nucleosides and deoxynucleoside ADNxt + HEXT -> ADN ncup1 Nucleosides and deoxynucleoside GSNxt + HEXT -> GSN ncup2 YBL042C FUI1 Uridine permease, Nucleosides and deoxynucleoside URIxt + HEXT -> URI ncup3 Nucleosides and deoxynucleoside CYTDxt + HEXT -> CYTD ncup4 Nucleosides and deoxynucleoside INSxt + HEXT -> INS ncup5 Nucleosides and deoxynucleoside DTxt + HEXT -> DT ncup7 Nucleosides and deoxynucleoside DINxt + HEXT -> DIN ncup8 Nucleosides and deoxynucleoside DGxt + HEXT -> DG ncup9 Nucleosides and deoxynucleoside DAxt + HEXT -> DA ncup10 Nucleosides and deoxynucleoside DCxt + HEXT -> DC ncup11 Nucleosides and deoxynucleoside DUxt + HEXT -> DU ncup12 Hypoxanthine HYXNxt + HEXT <-> HYXN hyxnup Xanthine XANxt <-> XAN xanup Metabolic By-Products YCR032W BPH1 Probable acetic acid export pump, Acetate transport ACxt + HEXT <-> AC acup Formate transport FORxt <-> FOR forup Ethanol transport ETHxt <-> ETH ethup Succinate transport SUCCxt + HEXT <-> SUCC succup YKL217W JEN1 Pyruvate lactate proton symport PYRxt + HEXT -> PYR jen1_1 Other Compounds YHL016C dur3 Urea active transport UREAxt + 2 HEXT <-> UREA dur3 YGR121C MEP1 Ammonia transport NH3xt <-> NH3 mep1 YNL142W MEP2 Ammonia transport, low capacity high affinity NH3xt <-> NH3 mep2 YPR138C MEP3 Ammonia transport, high capacity low affinity NH3xt <-> NH3 mep3 YJL129C trk1 Potassium transporter of the plasma membrane, high Kxt + HEXT <-> K trk1 affinity, member of the potassium transporter (TRK) family of membrane transporters YBR294W SUL1 Sulfate permease SLFxt -> SLF sul1 YLR092W SUL2 Sulfate permease SLFxt -> SLF sul2 YGR125W YGR125W Sulfate permease SLFxt -> SLF sulup YML123C pho84 Inorganic phosphate transporter, PIxt + HEXT <-> PI pho84 transmembrane protein Citrate CITxt + HEXT <-> CIT citup Dicarboxylates FUMxt + HEXT <-> FUM fumup Fatty acid transport C140xt -> C140 faup1 Fatty acid transport C160xt -> C160 faup2 Fatty acid transport C161xt -> C161 faup3 Fatty acid transport C180xt -> C180 faup4 Fatty acid transport C181xt -> C181 faup5 a-Ketoglutarate AKGxt + HEXT <-> AKG akgup YLR138W NHA1 Putative Na+/H+ antiporter NAxt <-> NA + HEXT nha1 YCR028C FEN2 Pantothenate PNTOxt + HEXT <-> PNTO fen2 ATP drain flux for constant maintanence requirements ATP -> ADP + PI atpmt YCR024c-a PMP1 H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmp1 YEL017c-a PMP2 H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmp2 YGL008c PMA1 H+-transporting P-type ATPase, ATP -> ADP + PI + HEXT pma1 major isoform, plasma membrane YPL036w PMA2 H+-transporting P-type ATPase, ATP -> ADP + PI + HEXT pma2 minor isoform, plasma membrane Glyceraldehyde transport GLALxt <-> GLAL glaltx Acetaldehyde transport ACALxt <-> ACAL acaltx YLR237W THI7 Thiamine transport protein THMxt + HEXT -> THIAMIN thm1 YOR071C YOR071C Probable low affinity thiamine transporter THMxt + HEXT -> THIAMIN thm2 YOR192C YOR192C Probable low affinity thiamine transporter THMxt + HEXT -> THIAMIN thm3 YIR028W dal4 ATNxt -> ATN dal4 YJR152W dal5 ATTxt -> ATT dal5 MTHNxt <-> MTHN mthup PAPxt <-> PAP papx DTTPxt <-> DTTP dttpx THYxt <-> THY + HEXT thyx GA6Pxt <-> GA6P ga6pup YGR065C VHT1 H+/biotin symporter and member of the allantoate BTxt + HEXT <-> BT btup permease family of the major facilitator superfamily AONAxt + HEXT <-> AONA kapaup DANNAxt + HEXT <-> DANNA dapaup OGTxt -> OGT ogtup SPRMxt -> SPRM sprmup PIMExt -> PIME pimeup Oxygen transport O2xt <-> O2 o2tx Carbon dioxide transport CO2xt <-> CO2 co2tx YOR011W AUS1 ERGOSTxt <-> ERGOST ergup YOR011W AUS1 Putative sterol transporter ZYMSTxt <-> ZYMST zymup RFLAVxt + HEXT -> RIBFLAV rflup

Standard chemical names for the acronyms used to identify the reactants in the reactions of Table 2 are provided in Table 3.

TABLE 3 Abbreviation Metabolite 13GLUCAN 1,3-beta-D-Glucan 13PDG 3-Phospho-D-glyceroyl phosphate 23DAACP 2,3-Dehydroacyl-[acyl-carrier-protein] 23PDG 2,3-Bisphospho-D-glycerate 2HDACP Hexadecenoyl-[acp] 2MANPD (“alpha”-D-mannosyl)(,2)-“beta”-D- mannosyl-diacetylchitobiosyldiphosphodolichol 2N6H 2-Nonaprenyl-6-hydroxyphenol 2NMHMB 3-Demethylubiquinone-9 2NMHMBm 3-Demethylubiquinone-9M 2NPMBm 2-Nonaprenyl-6-methoxy-1,4-benzoquinoneM 2NPMMBm 2-Nonaprenyl-3-methyl-6-methoxy-1,4- benzoquinoneM 2NPMP 2-Nonaprenyl-6-methoxyphenol 2NPMPm 2-Nonaprenyl-6-methoxyphenolM 2NPPP 2-Nonaprenylphenol 2PG 2-Phospho-D-glycerate 3DDAH7P 2-Dehydro-3-deoxy-D-arabino- heptonate 7-phosphate 3HPACP (3R)-3-Hydroxypalmitoyl- [acyl-carrier protein] 3PG 3-Phospho-D-glycerate 3PSER 3-Phosphoserine 3PSME 5-O-(1-Carboxyvinyl)-3-phosphoshikimate 4HBZ 4-Hydroxybenzoate 4HLT 4-Hydroxy-L-threonine 4HPP 3-(4-Hydroxyphenyl)pyruvate 4PPNCYS (R)-4′-Phosphopantothenoyl-L-cysteine 4PPNTE Pantetheine 4′-phosphate 4PPNTEm Pantetheine 4′-phosphateM 4PPNTO D-4′-Phosphopantothenate 5MTA 5′-Methylthioadenosine 6DGLC D-Gal alpha 1->6D-Glucose A6RP 5-Amino-6-ribitylamino-2,4 (1H, 3H)-pyrimidinedione A6RP5P 5-Amino-6-(5′-phosphoribosylamino)uracil A6RP5P2 5-Amino-6-(5′-phosphoribitylamino)uracil AACCOA Acetoacetyl-CoA AACP Acyl-[acyl-carrier-protein] AATRE6P alpha,alpha′-Trehalose 6-phosphate ABUTm 2-Aceto-2-hydroxybutyrateM AC Acetate ACACP Acyl-[acyl-carrier protein] ACACPm Acyl-[acyl-carrier protein]M ACAL Acetaldehyde ACALm AcetaldehydeM ACAR O-Acetylcarnitine ACARm O-AcetylcarnitineM ACCOA Acetyl-CoA ACCOAm Acetyl-CoAM ACLAC 2-Acetolactate ACLACm 2-AcetolactateM ACm AcetateM ACNL 3-Indoleacetonitrile ACOA Acyl-CoA ACP Acyl-carrier protein ACPm Acyl-carrier proteinM ACTAC Acetoacetate ACTACm AcetoacetateM ACYBUT gamma-Amino-gamma-cyanobutanoate AD Adenine ADCHOR 4-amino-4-deoxychorismate ADm AdenineM ADN Adenosine ADNm AdenonsineM ADP ADP ADPm ADPM ADPRIB ADPribose ADPRIBm ADPriboseM AGL3P Acyl-sn-glycerol 3-phosphate AHHMD 2-Amino-7,8-dihydro-4-hydroxy-6- (diphosphooxymethyl)pteridine AHHMP 2-Amino-4-hydroxy-6-hydroxymethyl- 7,8-dihydropteridine AHM 4-Amino-5-hydroxymethyl-2-methylpyrimidine AHMP 4-Amino-2-methyl-5-phosphomethylpyrimidine AHMPP 2-Methyl-4-amino-5-hydroxymethylpyrimidine diphosphate AHTD 2-Amino-4-hydroxy-6-(erythro-1,2,3- trihydroxypropyl)-dihydropteridine triphosphate AICAR 1-(5′-Phosphoribosyl)-5-amino-4- imidazolecarboxamide AIR Aminoimidazole ribotide AKA 2-Oxoadipate AKAm 2-OxoadipateM AKG 2-Oxoglutarate AKGm 2-OxoglutarateM AKP 2-Dehydropantoate AKPm 2-DehydropantoateM ALA L-Alanine ALAGLY R-S-Alanylglycine ALAm L-AlanineM ALAV 5-Aminolevulinate ALAVm 5-AminolevulinateM ALTRNA L-Arginyl-tRNA(Arg) AM6SA 2-Aminomuconate 6-semialdehyde AMA L-2-Aminoadipate AMASA L-2-Aminoadipate 6-semialdehyde AMG Methyl-D-glucoside AMP AMP AMPm AMPM AMUCO 2-Aminomuconate AN Anthranilate AONA 8-Amino-7-oxononanoate APEP Nalpha-Acetylpeptide APROA 3-Aminopropanal APROP alpha-Aminopropiononitrile APRUT N-Acetylputrescine APS Adenylylsulfate ARAB D-Arabinose ARABLAC D-Arabinono-1,4-lactone ARG L-Arginine ARGSUCC N-(L-Arginino)succinate ASER O-Acetyl-L-serine ASN L-Asparagine ASP L-Aspartate ASPERMD N1-Acetylspermidine ASPm L-AspartateM ASPRM N1-Acetylspermine ASPSA L-Aspartate 4-semialdehyde ASPTRNA L-Asparaginyl-tRNA(Asn) ASPTRNAm L-Asparaginyl-tRNA(Asn)M ASUC N6-(1,2-Dicarboxyethyl)-AMP AT3P2 Acyldihydroxyacetone phosphate ATN Allantoin ATP ATP ATPm ATPM ATRNA tRNA(Arg) ATRP P1,P4-Bis(5′-adenosyl)tetraphosphate ATT Allantoate bALA beta-Alamine BASP 4-Phospho-L-aspartate bDG6P beta-D-Glucose 6-phosphate bDGLC beta-D-Glucose BIO Biotin BT Biotin C100ACP Decanoyl-[acp] C120ACP Dodecanoyl-[acyl-carrier protein] C120ACPm Dodecanoyl-[acyl-carrier protein]M C140 Myristic acid C140ACP Myristoyl-[acyl-carrier protein] C140ACPm Myristoyl-[acyl-carrier protein]M C141ACP Tetradecenoyl-[acyl-carrier protein] C141ACPm Tetradecenoyl-[acyl-carrier protein]M C160 Palmitate C160ACP Hexadecanoyl-[acp] C160ACPm Hexadecanoyl-[acp]M C161 1-Hexadecene C161ACP Palmitoyl-[acyl-carrier protein] C161ACPm Palmitoyl-[acyl-carrier protein]M C16A C16_aldehydes C180 Stearate C180ACP Stearoyl-[acyl-carrier protein] C180ACPm Stearoyl-[acyl-carrier protein]M C181 1-Octadecene C181ACP Oleoyl-[acyl-carrier protein] C181ACPm Oleoyl-[acyl-carrier protein]M C182ACP Linolenoyl-[acyl-carrier protein] C182ACPm Linolenoyl-[acyl-carrier protein]M CAASP N-Carbamoyl-L-aspartate CAIR 1-(5-Phospho-D-ribosyl)-5-amino-4- imidazolecarboxylate CALH 2-(3-Carboxy-3-aminopropyl)-L-histidine cAMP 3′,5′-Cyclic AMP CAP Carbamoyl phosphate CAR Carnitine CARm CarnitineM CBHCAP 3-Isopropylmalate CBHCAPm 3-IsopropylmalateM cCMP 3′,5′-Cyclic CMP cdAMP 3′,5′-Cyclic dAMP CDP CDP CDPCHO CDPcholine CDPDG CDPdiacylglycerol CDPDGm CDPdiacylglycerolM CDPETN CDPethanolamine CLR2 Ceramide-2 CER3 Ceramide-3 CGLY Cys-Gly cGMP 3′,5′-Cyclic GMP CHCOA 6-Carboxyhexanoyl-CoA CHIT Chitin CHITO Chitosan CHO Choline CHOR Chorismate cIMP 3′,5′-Cyclic IMP CIT Citrate CITm CitrateM CITR L-Citrulline CLm CardiolipinM CMP CMP CMPm CMPM CMUSA 2-Amino-3-carboxymuconate semialdehyde CO2 CO2 CO2m CO2M COA CoA COAm CoAM CPAD5P 1-(2-Carboxyphenylamino)-1-deoxy- D-ribulose 5-phosphate CPP Coproporphyrinogen CTP CTP CTPm CTPM CYS L-Cysteine CYTD Cytidine CYTS Cytosine D45P1 1-Phosphatidyl-D-myo-inositol 4,5-bisphosphate D6PGC 6-Phospho-D-gluconate D6PGL D-Glucono-1,5-lactone 6-phosphate D6RP5P 2,5-Diamino-6-hydroxy-4-(5′- phosphoribosylamino)-pyrimidine D8RL 6,7-Dimethyl-8-(1-D-ribityl)lumazine DA Deoxyadenosine DADP dADP DAGLY Diacylglycerol DAMP dAMP dAMP dAMP DANNA 7,8-Diaminononanoate DAPRP 1,3-Diaminopropane DATP dATP DB4P L-3,4-Dihydroxy-2-butanone 4-phosphate DC Deoxycytidine DCDP dCDP DCMP dCMP DCTP dCTP DFUC alpha-D-Fucoside DG Deoxyguanosine DGDP dGDP DGMP dGMP DGPP Diacylglycerol pyrophosphate DGTP dGTP DHF Dihydrofolate DHFm DihydrofolateM DHMVAm (R)-2,3-dihydroxy-3-methylbutanoateM DHP 2-Amino-4-hydroxy-6-(D-erythro-1,2,3- trihydroxypropyl)-7,8-dihydropteridine DHPP Dihydroneopterin phosphate DHPT Dihydropteroate DHSK 3-Dehydroshikimate DHSP Sphinganine 1-phosphate DHSPH 3-Dehydrosphinganine DHVALm (R)-3-Hydroxy-3-methyl-2-oxobutanoateM DIMGP D-erythro-1-(Imidazol-4- yl)glycerol 3-phosphate DIN Deoxyinosine DIPEP Dipeptide DISAC1P 2,3-bis(3-hydroxytetradecanoyl)- D-glucosaminyl-1,6-beta-D-2,3-bis(3- hydroxytetradecanoyl)-beta-D- glucosaminyl 1-phosphate DLIPOm DihydrolipoamideM DMPP Dimethylallyl diphosphate DMZYMST 4,4-Dimethylzymosterol DOL Dolichol DOLMANP Dolichyl beta-D-mannosyl phosphate DOLP Dolichyl phosphate DOLPP Dehydrodolichol diphosphate DOROA (S)-Dihydroorotate DPCOA Dephospho-CoA DPCOAm Dephospho-CoAM DPTH 2-[3-Carboxy-3-(methylammonio) propyl]-L-histidine DQT 3-Dehydroquinate DR1P Deoxy-ribose 1-phosphate DR5P 2-Deoxy-D-ribose 5-phosphate DRIB Deoxyribose DSAM S-Adenosylmethioninamine D1 Thymidine DTB Dethiobiotin DTBm DethiobiotinM DTDP dTDP DTMP dTMP DTP 1-Deoxy-d-threo-2-pentulose DTTP dTTP DU Deoxyuridine DUDP dUDP DUMP dUMP DUTP dUTP E4P D-Erythrose 4-phosphate EPM Epimelibiose EPST Episterol ER4P 4-Phospho-D-erythronate ERGOST Ergosterol ERTEOL Ergosta-5,7,22,24(28)-tetraenol ERTROL Ergosta-5,7,24(28)-trienol FTH Ethanol FTHm EthanolM FTHM Ethanolamine F1P D-Fructose 1-phosphate F26P D-Fructose 2,6-bisphosphate F6P beta-D-Fructose 6-phosphate FAD FAD FADH2m FADH2M FADm FADM FALD Formaldehyde FDP beta-D-Fructose 1,6-bisphosphate FERIm Ferricytochrome cM FEROm Ferrocytochrome cM FEST Fecosterol FGAM 2-(Formamido)-N1-(5′-phosphoribosyl) acetamidine FGAR 5′-Phosphoribosyl-N-formylglycinamide FGT S-Formylglutathione FKYN L-Formylkynurenine FMN FMN FMNm FMNM FMRNAm N-Formylmethionyl-tRNAM FOR Formate FORm FormateM FPP trans,trans-Farnesyl diphosphate FRU D-Fructose FTHF 10-Formyltetrahydrofolate FTHFm 10-FormyltetrahydrofolateM FUACAC 4-Fumarylacetoacetate FUC beta-D-Fucose FUM Fumarate FUMm FumarateM G1P D-Glucose 1-phosphate G6P alpha-D-Glucose 6-phosphate GA1P D-Glucosamine 1-phosphate GA6P D-Glucosamine 6-phosphate GABA 4-Aminobutanoate GABAL 4-Aminobutyraldehyde GABALm 4-AminobutyraldehydeM GABAm 4-AminobutanoateM GAL1P D-Galactose 1-phosphate GAR 5′-Phosphoribosylglycinamide GBAD 4-Guanidino-butanamide GBAT 4-Guanidino-butanoate GC gamma-L-Glutamyl-L-cysteine GDP GDP GDPm GDPM GDPMAN GDPmannose GGL Galactosylglycerol GL Glycerol GL3P sn-Glycerol 3-phosphate GL3Pm sn-Glycerol 3-phosphateM GLAC D-Galactose GLACL 1-alpha-D-Galactosyl-myo-inositol GLAL Glycolaldehyde GLAM Glucosamine GLC alpha-D-Glucose GLCN Gluconate GLN L-Glutamine GLP Glycylpeptide GLT L-Glucitol GLU L-Glutamate GLUGSAL L-Glutamate 5-semialdehyde GLUGSALm L-Glutamate 5-semialdehydeM GLUm GlutamateM GLUP alpha-D-Glutamyl phosphate GLX Glyoxylate GLY Glycine GLYCOGEN Glycogen GLYm GlycineM GLYN Glycerone GMP GMP GN Guanine GNm GuanineM GPP Geranyl diphosphate GSN Guanosine GSNm GuanosineM GTP GTP GTPm GTPM GTRNA L-Glutamyl-tRNA(Glu) GTRNAm L-Glutamyl-tRNA(Glu)M GTRP P1,P4-Bis(5′-guanosyl)tetraphosphate H2O2 H2O2 H2S Hydrogen sulfide H2SO3 Sulfite H3MCOA (S)-3-Hydroxy-3-methylglutaryl-CoA H3MCOAm (S)-3-Hydroxy-3-methylglutaryl-CoAM HACNm But-1-ene-1,2,4-tricarboxylateM HACOA (3S)-3-Hydroxyacyl-CoA HAN 3-Hydroxyanthranilate HBA 4-Hydroxy-benzyl alcohol HCIT 2-Hydroxybutane-1,2,4-tricarboxylate HCITm 2-Hydroxybutane-1,2,4-tricarboxylateM HCYS Homocysteine HFXT H + EXT HHTRNA L-Histidyl-tRNA(His) HIB (S)-3-Hydroxyisobutyrate HIBCOA (S)-3-Hydroxyisobutyryl-CoA HICITm HomoisocitrateM HIS L-Histidine HISOL L-Histidinol HISOLP L-Histidinol phosphate HKYN 3-Hydroxykynurenine Hm H + M HMB Hydroxymethylbilane HOMOGEN Homogentisate HPRO trans-4-Hydroxy-L-proline HSER L-Homoserine HTRNA tRNA(His) HYXAN Hypoxanthine IAC Indole-3-acetate IAD Indole-3-acetamide IBCOA 2-Methylpropanoyl-CoA ICIT Isocitrate ICITm IsocitrateM IDP IDP IDPm IDPM IGP Indoleglycerol phosphate IGST 4,4-Dimethylcholesta-8,14,24-trienol IIMZYMST Intermediate_Methylzymosterol_II IIZYMST Intermediate_Zymosterol_II ILE L-Isoleucine ILEm L-IsoleucineM IMACP 3-(Imidazol-4-yl)-2-oxopropyl phosphate IMP IMP IMZYMST Intermediate_Methylzymosterol_I INAC Indoleacetate INS Inosine IPC Inositol phosphorylceramide IPPMAL 2-Isopropylmalate IPPMALm 2-IsopropylmalateM IPPP Isopentenyl diphosphate ISUCC a-Iminosuccinate ITCCOAm Itaconyl-CoAM ITCm ItaconateM ITP ITP ITPm ITPM IVCOA 3-Methylbutanoyl-CoA IZYMST Intermediate_Zymosterol_I K Potassium KYN L-Kynurenine LAC (R)-Lactate LACALm (S)-LactaldehydeM LACm (R)-LactateM LCCA a Long-chain carboxylic acid LLU L-Leucine LFUm L-LeucineM LGT (R)-S-Lactoylglutathione LGTm (R)-S-LactoylglutathioneM LIPIV 2,3,2′,3′-tetrakis(3-hydroxytetradecanoyl)- D-glucosaminyl-1,6-beta-D-glucosamine 1,4′- bisphosphate LIPOm LipoamideM LIPX Lipid X LLACm (S)-LactateM LLCT L-Cystathionine LLTRNA L-lysyl-tRNA(Lys) LLTRNAm L-lysyl-tRNA(Lys)M LNST Lanosterol LIRNA tRNA(Lys) LIRNAm tRNA(Lys)M LYS L-Lysine LYSm L-LysineM MAACOA a-Methylacetoacetyl-CoA MACAC 4-Maleylacetoacetate MACOA 2-Methylprop-2-enoyl-CoA MAL Malate MALACP Malonyl-[acyl-carrier protein] MALACPm Malonyl-[acyl-carrier protein]M MALCOA Malonyl-CoA MALm MalateM MALT Malonate MALTm MalonateM MAN alpha-D-Mannose MANIP alpha-D-Mannose 1-phosphate MAN2PD beta-D-Mannosyldiacetylchitobiosyldiphosphodolichol MAN6P D-Mannose 6-phosphate MANNAN Mannan MBCOA Methylbutyryl-CoA MCCOA 2-Methylbut-2-enoyl-CoA MCRCOA 2-Methylbut-2-enoyl-CoA MDAP Meso-diaminopimelate MELI Melibiose MELT Melibutol MFT L-Methionine METH Methanethiol METHF 5,10-Methenyltetrahydrofolate METHFm 5,10-MethenyltetrahydrofolateM METTHF 5,10-Methylenetetrahydrofolate METTHFm 5,10-MethylenetetrahydrofolateM MGCOA 3-Methylglutaconyl-CoA MHIS N(pai)-Methyl-L-histidine MHVCOA a-Methyl-b-hydroxyvaleryl-CoA MI myo-Inositol MI1P 1L-myo-Inositol 1-phosphate MIP2C Inositol-mannose-P-inositol-P-ceramide MIPC Mannose-inositol-P-ceramide MK Menaquinone MLT Maltose MMCOA Methylmalonyl-CoA MMET S-Methylmethionine MMS (S)-Methylmalonate semialdehyde MNT D-Mannitol MNT6P D-Mannitol 1-phosphate MTHF 5-Methyltetrahydrofolate MTHFm 5-MethyltetrahydrofolateM MTHGXL Methylglyoxal MTHN Methane MTHNm MethaneM MTHPTGLU 5-Methyltetrahydropteroyltri-L-glutamate MTRNAm L-Methionyl-tRNAM MVL (R)-Mevalonate MVLm (R)-MevalonateM MYOI myo-Inositol MZYMST 4-Methylzymsterol N4HBZ 3-Nonaprenyl-4-hydroxybenzoate NA Sodium NAAD Deamino-NAD+ NAADm Deamino-NAD + M NAC Nicotinate NACm NicotinateM NAD NAD+ NADH NADH NADHm NADHM NADm NAD + M NADP NADP+ NADPH NADPH NADPHm NADPHM NADPm NADP + M NAG N-Acetylglucosamine NAGA1P N-Acetyl-D-glucosamine 1-phosphate NAGA6P N-Acetyl-D-glucosamine 6-phosphate NAGLUm N-Acetyl-L-glutamateM NAGLUPm N-Acetyl-L-glutamate 5-phosphateM NAGLUSm N-Acetyl-L-glutamate 5-semialdehydeM NAM Nicotinamide NAMm NicotinamideM NAMN Nicotinate D-ribonucleotide NAMNm Nicotinate D-ribonucleotideM NAORNm N2-Acetyl-L-ornithineM NH3 NH3 NH3m NH3M NH4 NH4+ NPP all-trans-Nonaprenyldiphosphate NPPm all-trans-NonaprenyldiphosphateM NPRAN N-(5-Phospho-D-ribosyl)anthranilate O2 Oxygen O2m OxygenM OA Oxaloacetate OACOA 3-Oxoacyl-CoA OAHSER O-Acetyl-L-homoserine OAm OxaloacetateM OBUT 2-Oxobutanoate OBUTm 2-OxobutanoateM OFP Oxidized flavoprotein OGT Oxidized glutathione OHB 2-Oxo-3-hydroxy-4-phosphobutanoate OHm HO-M OICAP 3-Carboxy-4-methyl-2-oxopentanoate OICAPm 3-Carboxy-4-methyl-2-oxopentanoateM OIVAL (R)-2-Oxoisovalerate OIVALm (R)-2-OxoisovalerateM OMP Orotidine 5′-phosphate OMVAL 3-Methyl-2-oxobutanoate OMVALm 3-Methyl-2-oxobutanoateM OPEP Oligopeptide ORN L-Ornithine ORNm L-OrnithineM OROA Orotate OSLHSER O-Succinyl-L-homoserine OSUC Oxalosuccinate OSUCm OxalosuccinateM OTHIO Oxidized thioredoxin OTHIOm Oxidized thioredoxinM OXA Oxaloglutarate OXAm OxaloglutarateM P5C (S)-1-Pyrroline-5-carboxylate P5Cm (S)-1-Pyrroline-5-carboxylateM P5P Pyridoxine phosphate PA Phosphatidate PABA 4-Aminobenzoate PAC Phenylacetic acid PAD 2-Phenylacetamide PALCOA Palmitoyl-CoA PAm PhosphatidateM PANT (R)-Pantoate PANTm (R)-PantoateM PAP Adenosine 3′,5′-bisphosphate PAPS 3′-Phosphoadenylylsulfate PBG Porphobilinogen PC Phosphatidylcholine PC2 Sirohydrochlorin PCHO Choline phosphate PDLA Pyridoxamine PDLA5P Pyridoxamine phosphate PDME Phosphatidyl-N-dimethylethanolamine PE Phosphatidylethanolamine PEm PhosphatidylethanolamineM PEP Phosphoenolpyruvate PEPD Peptide PEPm PhosphoenolpyruvateM PLPT Peptide PETHM Ethanolamine phosphate PGm PhosphatidylglycerolM PGPm PhosphatidylglycerophosphateM PHC L-1-Pyrroline-3-hydroxy-5-carboxylate PHE L-Phenylalanine PHEN Prephenate PHP 3-Phosphonooxypyruvate PHPYR Phenylpyruvate PHSER O-Phospho-L-homoserine PHSP Phytosphingosine 1-phosphate PHT O-Phospho-4-hydroxy-L-threonine PI Orthophosphate PIm OrthophosphateM PIME Pimelic Acid PINS 1-Phosphatidyl-D-myo-inositol PINS4P 1-Phosphatidyl-1D-myo-inositol 4-phosphate PINSP 1-Phosphatidyl-1D-myo-inositol 3-phosphate PL Pyridoxal PL5P Pyridoxal phosphate PMME Phosphatidyl-N-methylethanolamine PMVL (R)-5-Phosphomevalonate PNTO (R)-Pantothenate PPHG Protoporphyrinogen IX PPHGm Protoporphyrinogen IXM PPI Pyrophosphate PPIm PyrophosphateM PPIXm ProtoporphyrinM PPMAL 2-Isopropylmaleate PPMVL (R)-5-Diphosphomevalonate PRAM 5-Phosphoribosylamine PRBAMP N1-(5-Phospho-D-ribosyl)-AMP PRBATP N1-(5-Phospho-D-ribosyl)-ATP PRFICA 1-(5′-Phosphoribosyl)-5-formamido-4- imidazolecarboxamide PRFP 5-(5-Phospho-D-ribosylaminoformimino)- 1-(5-phosphoribosyl)-imidazole-4-carboxamide PRLP N-(5′-Phospho-D-1′-ribulosylformimino)-5-amino- 1-(5″-phospho-D-ribosyl)-4-imidazolecarboxamide PRO L-Proline PROm L-ProlineM PROPCOA Propanoyl-CoA PRPP 5-Phospho-alpha-D-ribose 1-diphosphate PRPPm 5-Phospho-alpha-D-ribose 1-diphosphateM PS Phosphatidylserine PSm PhosphatidylserineM PSPH Phytosphingosine PIHm HemeM PIRC Putrescine PTRSC Putrescine PUR15P Pseudouridine 5′-phosphate PYR Pyruvate PYRDX Pyridoxine PYRm PyruvateM Q Ubiquinone-9 QA Pyridine-2,3-dicarboxylate QAm Pyridine-2,3-dicarboxylateM QH2 Ubiquinol QH2m UbiquinolM Qm Ubiquinone-9M R1P D-Ribose 1-phosphate R5P D-Ribose 5-phosphate RADP 4-(1-D-Ribitylamino)-5-amino-2,6-dihydroxypyrimidine RAF Raffinose RFP Reduced flavoprotein RGT Glutathione RGTm GlutathioneM RIB D-Ribose RIBFLAVm RiboflavinM RIBOFLAV Riboflavin RIPm alpha-D-Ribose 1-phosphateM RL5P D-Ribulose 5-phosphate RMN D-Rhamnose RTHIO Reduced thioredoxin RTHIOm Reduced thioredoxinM S Sulfur S17P Sedoheptulose 1,7-bisphosphate S23E (S)-2,3-Epoxysqualene S7P Sedoheptulose 7-phosphate SACP N6-(L-1,3-Dicarboxypropyl)-L-lysine SAH S-Adenosyl-L-homocysteine SAHm S-Adenosyl-L-homocysteineM SAICAR 1-(5′-Phosphoribosyl)-5-amino-4-(N- succinocarboxamide)-imidazole SAM S-Adenosyl-L-methionine SAMm S-Adenosyl-L-methionineM SAMOB S-Adenosyl-4-methylthio-2-oxobutanoate SAPm S-AminomethyldihydrolipoylproteinM SER L-Serine SERm L-SerineM SLF Sulfate SLFm SulfateM SME Shikimate SME5P Shikimate 3-phosphate SOR Sorbose SOR1P Sorbose 1-phosphate SOT D-Sorbitol SPH Sphinganine SPMD Spermidine SPRM Spermine SPRMD Spermidine SQL Squalene SUC Sucrose SUCC Succinate SUCCm SuccinateM SUCCOAm Succinyl-CoAM SUCCSAL Succinate semialdehyde T3P1 D-Glyceraldehyde 3-phosphate T3P2 Glycerone phosphate T3P2m Glycerone phosphateM TAG16P D-Tagatose 1,6-bisphosphate TAG6P D-Tagatose 6-phosphate TAGLY Triacylglycerol TCOA Tetradecanoyl-CoA TGLP N-Tetradecanoylglycylpeptide THF Tetrahydrofolate THFG Tetrahydrofolyl-[Glu](n) THFm TetrahydrofolateM THIAMIN Thiamin THMP Thiamin monophosphate THPTGLU Tetrahydropteroyltri-L-glutamate THR L-Threonine THRm L-ThreonineM THY Thymine THZ 5-(2-Hydroxyethyl)-4-methylthiazole THZP 4-Methyl-5-(2-phosphoethyl)-thiazole TP1 D-myo-inositol 1,4,5-trisphosphate TPP Thiamin diphosphate TPPP Thiamin triphosphate TRE alpha,alpha-Trehalose TRE6P alpha,alpha′-Trehalose 6-phosphate TRNA tRNA TRNAG tRNA(Glu) TRNAGm tRNA(Glu)M TRNAm tRNAM TRP L-Tryptophan TRPm L-TryptophanM TRPTRNAm L-Tryptophanyl-tRNA(Trp)M TYR L-Tyrosine UDP UDP UDPG UDPglucose UDPG23A UDP-2,3-bis(3-hydroxytetradecanoyl)glucosamine UDPG2A UDP-3-O-(3-hydroxytetradecanoyl)-D-glucosamine UDPG2AA UDP-3-O-(3-hydroxytetradecanoyl)-N- acetylglucosamine UDPGAL UDP-D-galactose UDPNAG UDP-N-acetyl-D-galactosamine UDPP Undecaprenyl diphosphate UGC (−)-Ureidoglycolate UMP UMP UPRG Uroporphyrinogen III URA Uracil UREA Urea UREAC Urea-1-carboxylate URI Uridine UTP UTP VAL L-Valine X5P D-Xylose-5-phosphate XAN Xanthine XMP Xanthosine 5′-phosphate XTSINE Xanthosine XTSN Xanthosine XUL D-Xylulose XYL D-Xylose ZYMST Zymosterol

Depending upon the particular environmental conditions being tested and the desired activity, a reaction network data structure can contain smaller numbers of reactions such as at least 200, 150, 100 or 50 reactions. A reaction network data structure having relatively few reactions can provide the advantage of reducing computation time and resources required to perform a simulation. When desired, a reaction network data structure having a particular subset of reactions can be made or used in which reactions that are not relevant to the particular simulation are omitted. Alternatively, larger numbers of reactions can be included in order to increase the accuracy or molecular detail of the methods of the invention or to suit a particular application. Thus, a reaction network data structure can contain at least 300, 350, 400, 450, 500, 550, 600 or more reactions up to the number of reactions that occur in or by S. cerevisiae or that are desired to simulate the activity of the full set of reactions occurring in S. cerevisiae. A reaction network data structure that is substantially complete with respect to the metabolic reactions of S. cerevisiae provides the advantage of being relevant to a wide range of conditions to be simulated, whereas those with smaller numbers of metabolic reactions are limited to a particular subset of conditions to be simulated.

A S. cerevisiae reaction network data structure can include one or more reactions that occur in or by S. cerevisiae and that do not occur, either naturally or following manipulation, in or by another prokaryotic organism, such as Escherichia coli, Haemophilus influenzae, Bacillus subtilis, Helicobacter pylori or in or by another eukaryotic organism, such as Homo sapiens. Examples of reactions that are unique to S. cerevisiae compared at least to Escherichia coli, Haemophilus influenzae, and Helicobacter pylori include those identified in Table 4. It is understood that a S. cerevisiae reaction network data structure can also include one or more reactions that occur in another organism. Addition of such heterologous reactions to a reaction network data structure of the invention can be used in methods to predict the consequences of heterologous gene transfer in S. cerevisiae, for example, when designing or engineering man-made cells or strains.

TABLE 4 Reactions specific to S. cerevisiae metabolic network glk1_3, hxk1_1, hxk2_1, hxk1_4, hxk2_4, pfk1_3, idh1, idp1_1, idp1_2, idp2_1, idp3_1, idp2_2, idp3_2, lsc1R, pyc1, pyc2, cyb2, dld1, ncp1, cytr_, cyto, atp1, pma1, pma2, pmp1, pmp2, cox1, rbk1_2, ach1_1, ach1_2, sfa1_1R, unkrx11R, pdc1, pdc5, pdc6, lys20, adh1R, adh3R, adh2R, adh4R, adh5R, sfa1_2R, psa1, pfk26, pfk27, fbp26, gal7R mel1_2, mel1_3, mel1_4R, mel1_5R, mel1_6R, mel1_7R, fsp2b, sor1, gsy1, gsy2, fks1, fks3, gsc2, tps1, tps3, tsl1, tps2, ath1, nth1, nth2, fdh1, tfo1a, tfo1b, dur1R, dur2, nit2, cyr1, guk1_3R, ade2R, pde1, pde2_1, pde2_2, pde2_3, pde2_4, pde2_5, apa2, apa1_1, apa1_3, apa1_2R, ura2_1, ura4R, ura1_1R, ura10R, ura5R, ura3, npkR, fur1, fcy1, tdk1, tdk2, urk1_1, urk1_2, urk1_3, deoa1R, deoa2R, cdd1_1, cdd1_2, cdc8R, dut1, cdc21, cmka2R, dcd1R, ura7_2, ura8_2, deg1R, pus1R, pus2R, pus4R, ura1_2R, ara1_1, ara1_2, gna1R, pcm1aR, qri1R, chs1, chs2, chs3, put2_1, put2, glt1, gdh2, cat2, yat1, mht1, sam4, ecm40_2, cpa2, ura2_2, arg3, spe3, spe4, amd, amd2_1, atrna, msr1, rnas, ded81, hom6_1, cys4, gly1, agtR, gcv2R, sah1, met6, cys3, met17_1, met17hR, dph5, met3, met14, met17_2, met17_3, lys21, lys20a, lys3R, lys4R, lys12R, lys12bR, amitR, lys2_1, lys2_2, lys9R, lys1aR, krs1, msk1, pro2_1, gps1R, gps2R, pro3_3, pro3_4, pro3_1, pro3_5, dal1R, dal2R, dal3R, his4_3, hts1, hmt1, tyr1, cta1, ctt1, ald6, ald4_2, ald5_1, tdo2, kfor_, kynu_1, kmo, kynu_2, bna1, aaaa, aaab, aaac, tyrdega, tyrdegb, tyrdegc, trydegd, msw1, amd2_2, amd2_3, spra, sprb, sprc, sprd, spre, dys1, leu4, leu1_2R, pclig, xapa1R, xapa2R, xapa3R, ynk1_6R, ynk1_9R, udpR, pyrh1R, pyrh2R, cmpg, usha1, usha2, usha5, usha6, usha11, gpx1R, gpx2R, hyr1R, ecm38, nit2_1, nit2_2, nmt1, nat1, nat2, bgl2, exg1, exg2, spr1, thi80_1, thi80_2, unkrxn8, pho11, fmn1_1, fmn1_2, pdx3_2R, pdx3_3R, pdx3_4R, pdx3_1, pdx3_5, bio1, fol1_4, ftfa, ftfb, fol3R, met7R, rma1R, met12, met13, mis1_2, ade3_2, mtd1, fmt1, TypeII_1, TypeII_2, TypeII_4, TypeII_3, TypeII_6, TypeII_5, TypeII_9, TypeII_8, TypeII_7, c100sn, c180sy, c182sy, faa1R, faa2R, faa3R, faa4R, fox2bR, pot1_1, erg10_1R, erg10_2R, Gat1_2, Gat2_2, ADHAPR, AGAT, slc1, Gat1_1, Gat2_1, cho1aR, cho1bR, cho2, opi3_1, opi3_2, cki1, pct1, cpt1, eki1, ect1, ept1R, ino1, impa1, pis1, tor1, tor2, vps34, pik1, sst4, fab1, mss4, plc1, pgs1R, crd1, dpp1, lpp1, hmgsR, hmg1R, hmg2R, erg12_1, erg12_2, erg12_3, erg12_4, erg8, mvd1, erg9, erg1, erg7, unkrxn3, unkrxn4, cdisoa, erg11_1, erg_24, erg25_1, erg26_1, erg11_2, erg25_2, erg26_2, erg11_3, erg6, erg2, erg3, erg5, erg4, lcb1, lcb2, tsc10, sur2, csyna, csynb, scs7, aur1, csg2, sur1, ipt1, lcb4_1, lcb5_1, lcb4_2, lcb5_2, lcb3, ysr3, dp11, sec59, dpm1, pmt1, pmt2, pmt3, pmt4, pmt5, pmt6, kre2, ktr1, ktr2, ktr3, ktr4, ktr6, yur1, hor2, rhr2, cda1, cda2, daga, dak1, dak2, gpd1, nadg1R, nadg2R, npt1, nadi, mnadphps, mnadg1R, mnadg2R, mnpt1, mnadi, hem1, bet2, coq1, coq2, cox10, ram1, rer2, srt1, mo2R, mco2R, methR, mmthnR, mnh3R, mthfR, mmthfR, mserR, mglyR, mcbhR, moicapR, mproR, mcmpR, macR, macar_, mcar_, maclacR, mactcR, moiva1R, momva1R, mpma1RR, ms1f, mthrR, maka, aac1, aac3, pet9, mir1aR, mir1dR, dic1_2R, dic_1R, dic1_3, mm1tR, moabR, ctp1_1R, ctp1_2R, ctp1_3R, pyrcaR, mlacR, gcaR, gcb, ort1R, crc1, gut2, gpd2, mt3p, mg13p, mfad, mriboR, mdtbR, mmcoaR, mmv1R, mpaR, mppntR, madR, mprppR, mdhfR, mqaR, moppR, msamR, msahR, sfc1, odc1R, odc2R, hxt1_2, hxt10_2, hxt11_2, hxt13_2, hxt15_2, hxt16_2, hxt17_2, hxt2_2, hxt3_2, hxt4_2, hxt5_2, hxt6_2, hxt7_2, hxt8_5, hxt9_2, sucup, akmupR, sorupR, arbup1R, gltlupb, gal2_3, hxt1_1, hxt10_1, hxt11, hxt11_1, hxt13_1, hxt15_1, hxt16_1, hxt17_1, hxt2_1, hxt3_1, hxt4, hxt4_1, hxt5_1, hxt6_1, hxt7_1, hxt8_4, hxt9_1, stl1_1, gaupR, mmp1, mltup, mntup, nagup, rmnup, ribup, treup_2, treup_1, xylupR, uga5, bap2_1R, bap3_1R, gap5R, gnp3R, tat7R, vap7R, sam3, put7, uga4, dip9R, gap22R, gap7R, gnp1R, gap23R, gap9R, hip1R, vap6R, bap2_4R, bap3_4R, gap13R, gap26R, gnp4R, mup1R, mup3R, bap2_5R, bap3_5R, gap14R, gap29R, tat4R, ptrup, sprup1, ptr2, ptr3, ptr4, mnadd2, fcy2_3R, fcy21_3R, fcy22_3R, gnupR, hyxnupR, nccup3, nccup4, nccup6, nccup7, ncgup4, ncgup7, ncgup11, ncgup12, ncup4, ncup7, ncup11, ncup12, ethupR, su11, su12, sulup, citupR, amgupR, atpmt, glaltxR, dal4, dal5, mthupR, papxR, thyxR, ga6pupR, btupR, kapaupR, dapaupR, ogtup, sprmup, pimeup, thm1, thm2, thm3, rflup, hnm1, ergupR, zymupR, hxt1_5, hxt10_3, hxt11_3, hxt13_3, hxt15_3, hxt16_3, hxt17_3, hxt2_3, hxt3_3, hxt4_3, hxt5_3, hxt6_3, hxt7_3, hxt8_6, hxt9_3, itr1, itr2, bio5a, agp2R, dttpxR, gltup

A reaction network data structure or index of reactions used in the data structure such as that available in a metabolic reaction database, as described above, can be annotated to include information about a particular reaction. A reaction can be annotated to indicate, for example, assignment of the reaction to a protein, macromolecule or enzyme that performs the reaction, assignment of a gene(s) that codes for the protein, macromolecule or enzyme, the Enzyme Commission (EC) number of the particular metabolic reaction or Gene Ontology (GO) number of the particular metabolic reaction, a subset of reactions to which the reaction belongs, citations to references from which information was obtained, or a level of confidence with which a reaction is believed to occur in S. cerevisiae. A computer readable medium or media of the invention can include a gene database containing annotated reactions. Such information can be obtained during the course of building a metabolic reaction database or model of the invention as described below.

As used herein, the term “gene database” is intended to mean a computer readable medium or media that contains at least one reaction that is annotated to assign a reaction to one or more macromolecules that perform the reaction or to assign one or more nucleic acid that encodes the one or more macromolecules that perform the reaction. A gene database can contain a plurality of reactions some or all of which are annotated. An annotation can include, for example, a name for a macromolecule; assignment of a function to a macromolecule; assignment of an organism that contains the macromolecule or produces the macromolecule; assignment of a subcellular location for the macromolecule; assignment of conditions under which a macromolecule is being expressed or being degraded; an amino acid or nucleotide sequence for the macromolecule; or any other annotation found for a macromolecule in a genome database such as those that can be found in Saccharomyces Genome Database maintained by Stanford University, or Comprehensive Yeast Genome Database maintained by MIPS.

A gene database of the invention can include a substantially complete collection of genes and/or open reading frames in S. cerevisiae or a substantially complete collection of the macromolecules encoded by the S. cerevisiae genome. Alternatively, a gene database can include a portion of genes or open reading frames in S. cerevisiae or a portion of the macromolecules encoded by the S. cerevisiae genome. The portion can be at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the genes or open reading frames encoded by the S. cerevisiae genome, or the macromolecules encoded therein. A gene database can also include macromolecules encoded by at least a portion of the nucleotide sequence for the S. cerevisiae genome such as at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the S. cerevisiae genome. Accordingly, a computer readable medium or media of the invention can include at least one reaction for each macromolecule encoded by a portion of the S. cerevisiae genome.

An in silico S. cerevisiae model according to the invention can be built by an iterative process which includes gathering information regarding particular reactions to be added to a model, representing the reactions in a reaction network data structure, and performing preliminary simulations wherein a set of constraints is placed on the reaction network and the output evaluated to identify errors in the network. Errors in the network such as gaps that lead to non-natural accumulation or consumption of a particular metabolite can be identified as described below and simulations repeated until a desired performance of the model is attained. An exemplary method for iterative model construction is provided in Example I.

Thus, the invention provides a method for making a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions in a computer readable medium or media. The method includes the steps of: (a) identifying a plurality of S. cerevisiae reactions and a plurality of S. cerevisiae reactants that are substrates and products of the S. cerevisiae reactions; (b) relating the plurality of S. cerevisiae reactants to the plurality of S. cerevisiae reactions in a data structure, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (c) making a constraint set for the plurality of S. cerevisiae reactions; (d) providing an objective function; (e) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, and (f) if at least one flux distribution is not predictive of S. cerevisiae physiology, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if at least one flux distribution is predictive of S. cerevisiae physiology, then storing the data structure in a computer readable medium or media.

Information to be included in a data structure of the invention can be gathered from a variety of sources including, for example, the scientific literature or an annotated genome sequence of S. cerevisiae such as the GENBANK™, a site maintained by the NCBI (ncbi.nlm.gov), the CYGD database, a site maintained by MIPS, or the SGD database, a site maintained by the School of Medicine at Stanford University, etc.

In the course of developing an in silico model of S. cerevisiae metabolism, the types of data that can be considered include, for example, biochemical information which is information related to the experimental characterization of a chemical reaction, often directly indicating a protein(s) associated with a reaction and the stoichiometry of the reaction or indirectly demonstrating the existence of a reaction occurring within a cellular extract; genetic information which is information related to the experimental identification and genetic characterization of a gene(s) shown to code for a particular protein(s) implicated in carrying out a biochemical event; genomic information which is information related to the identification of an open reading frame and functional assignment, through computational sequence analysis, that is then linked to a protein performing a biochemical event; physiological information which is information related to overall cellular physiology, fitness characteristics, substrate utilization, and phenotyping results, which provide evidence of the assimilation or dissimilation of a compound used to infer the presence of specific biochemical event (in particular translocations); and modeling information which is information generated through the course of simulating activity of S. cerevisiae using methods such as those described herein which lead to predictions regarding the status of a reaction such as whether or not the reaction is required to fulfill certain demands placed on a metabolic network.

The majority of the reactions occurring in S. cerevisiae reaction networks are catalyzed by enzymes/proteins, which are created through the transcription and translation of the genes found on the chromosome(s) in the cell. The remaining reactions occur through non-enzymatic processes. Furthermore, a reaction network data structure can contain reactions that add or delete steps to or from a particular reaction pathway. For example, reactions can be added to optimize or improve performance of a S. cerevisiae model in view of empirically observed activity. Alternatively, reactions can be deleted to remove intermediate steps in a pathway when the intermediate steps are not necessary to model flux through the pathway. For example, if a pathway contains 3 nonbranched steps, the reactions can be combined or added together to give a net reaction, thereby reducing memory required to store the reaction network data structure and the computational resources required for manipulation of the data structure. An example of a combined reaction is that for fatty acid degradation shown in Table 2, which combines the reactions for acyl-CoA oxidase, hydratase-dehydrogenase-epimerase, and acetyl-CoA C-acyltransferase of beta-oxidation of fatty acids.

The reactions that occur due to the activity of gene-encoded enzymes can be obtained from a genome database that lists genes or open reading frames identified from genome sequencing and subsequent genome annotation. Genome annotation consists of the locations of open reading frames and assignment of function from homology to other known genes or empirically determined activity. Such a genome database can be acquired through public or private databases containing annotated S. cerevisiae nucleic acid or protein sequences. If desired, a model developer can perform a network reconstruction and establish the model content associations between the genes, proteins, and reactions as described, for example, in Covert et al. Trends in Biochemical Sciences 26:179-186 (2001) and Palsson, WO 00/46405.

As reactions are added to a reaction network data structure or metabolic reaction database, those having known or putative associations to the proteins/enzymes which enable/catalyze the reaction and the associated genes that code for these proteins can be identified by annotation. Accordingly, the appropriate associations for some or all of the reactions to their related proteins or genes or both can be assigned. These associations can be used to capture the non-linear relationship between the genes and proteins as well as between proteins and reactions. In some cases, one gene codes for one protein which then perform one reaction. However, often there are multiple genes which are required to create an active enzyme complex and often there are multiple reactions that can be carried out by one protein or multiple proteins that can carry out the same reaction. These associations capture the logic (i.e. AND or OR relationships) within the associations. Annotating a metabolic reaction database with these associations can allow the methods to be used to determine the effects of adding or eliminating a particular reaction not only at the reaction level, but at the genetic or protein level in the context of running a simulation or predicting S. cerevisiae activity.

A reaction network data structure of the invention can be used to determine the activity of one or more reactions in a plurality of S. cerevisiae reactions independent of any knowledge or annotation of the identity of the protein that performs the reaction or the gene encoding the protein. A model that is annotated with gene or protein identities can include reactions for which a protein or encoding gene is not assigned. While a large portion of the reactions in a cellular metabolic network are associated with genes in the organism's genome, there are also a substantial number of reactions included in a model for which there are no known genetic associations. Such reactions can be added to a reaction database based upon other information that is not necessarily related to genetics such as biochemical or cell based measurements or theoretical considerations based on observed biochemical or cellular activity. For example, there are many reactions that are not protein-enabled reactions. Furthermore, the occurrence of a particular reaction in a cell for which no associated proteins or genetics have been currently identified can be indicated during the course of model building by the iterative model building methods of the invention.

The reactions in a reaction network data structure or reaction database can be assigned to subsystems by annotation, if desired. The reactions can be subdivided according to biological criteria, such as according to traditionally identified metabolic pathways (glycolysis, amino acid metabolism and the like) or according to mathematical or computational criteria that facilitate manipulation of a model that incorporates or manipulates the reactions. Methods and criteria for subdividing a reaction database are described in further detail in Schilling et al., J. Theor. Biol. 203:249-283 (2000). The use of subsystems can be advantageous for a number of analysis methods, such as extreme pathway analysis, and can make the management of model content easier. Although assigning reactions to subsystems can be achieved without affecting the use of the entire model for simulation, assigning reactions to subsystems can allow a user to search for reactions in a particular subsystem, which may be useful in performing various types of analyses. Therefore, a reaction network data structure can include any number of desired subsystems including, for example, 2 or more subsystems, 5 or more subsystems, 10 or more subsystems, 25 or more subsystems or 50 or more subsystems.

The reactions in a reaction network data structure or metabolic reaction database can be annotated with a value indicating the confidence with which the reaction is believed to occur in S. cerevisiae. The level of confidence can be, for example, a function of the amount and form of supporting data that is available. This data can come in various forms including published literature, documented experimental results, or results of computational analyses. Furthermore, the data can provide direct or indirect evidence for the existence of a chemical reaction in a cell based on genetic, biochemical, and/or physiological data.

The invention further provides a computer readable medium, containing (a) a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product, and (b) a constraint set for the plurality of S. cerevisiae reactions.

Constraints can be placed on the value of any of the fluxes in the metabolic network using a constraint set. These constraints can be representative of a minimum or maximum allowable flux through a given reaction, possibly resulting from a limited amount of an enzyme present. Additionally, the constraints can determine the direction or reversibility of any of the reactions or transport fluxes in the reaction network data structure. Based on the in vivo environment where S. cerevisiae lives the metabolic resources available to the cell for biosynthesis of essential molecules for can be determined. Allowing the corresponding transport fluxes to be active provides the in silico S. cerevisiae with inputs and outputs for substrates and by-products produced by the metabolic network.

Returning to the hypothetical reaction network shown in FIG. 1, constraints can be placed on each reaction in the exemplary format, shown in FIG. 3, as follows. The constraints are provided in a format that can be used to constrain the reactions of the stoichiometric matrix shown in FIG. 2. The format for the constraints used for a matrix or in linear programming can be conveniently represented as a linear inequality such as β_(J)≦ν_(J)≦α_(j):j=1 . . . n  (Eq. 1) where ν_(J) is the metabolic flux vector, β_(j) is the minimum flux value and α_(j) is the maximum flux value. Thus, α_(j) can take on a finite value representing a maximum allowable flux through a given reaction or β_(J) can take on a finite value representing minimum allowable flux through a given reaction. Additionally, if one chooses to leave certain reversible reactions or transport fluxes to operate in a forward and reverse manner the flux may remain unconstrained by setting β_(j) to negative infinity and α_(j) to positive infinity as shown for reaction R₂ in FIG. 3. If reactions proceed only in the forward reaction β_(j) is set to zero while α_(j) is set to positive infinity as shown for reactions R₁, R₃, R₄, R₅, and R₆ in FIG. 3. As an example, to simulate the event of a genetic deletion or non-expression of a particular protein, the flux through all of the corresponding metabolic reactions related to the gene or protein in question are reduced to zero by setting α_(j) and β_(J) to be zero. Furthermore, if one wishes to simulate the absence of a particular growth substrate, one can simply constrain the corresponding transport fluxes that allow the metabolite to enter the cell to be zero by setting α_(j) and β_(j) to be zero. On the other hand if a substrate is only allowed to enter or exit the cell via transport mechanisms, the corresponding fluxes can be properly constrained to reflect this scenario.

The in silico S. cerevisiae model and methods described herein can be implemented on any conventional host computer system, such as those based on Intel.RTM. microprocessors and running Microsoft Windows operating systems. Other systems, such as those using the UNIX or LINUX operating system and based on IBM.RTM., DEC.RTM. or Motorola.RTM. microprocessors are also contemplated. The systems and methods described herein can also be implemented to run on client-server systems and wide-area networks, such as the Internet.

Software to implement a method or model of the invention can be written in any well-known computer language, such as JAVA™, C, C++, VISUAL BASIC™, FORTRAN or COBOL™ and compiled using any well-known compatible compiler. The software of the invention normally runs from instructions stored in a memory on a host computer system. A memory or computer readable medium can be a hard disk floppy disc, compact disc, magneto-optical disc, Random Access Memory, Read Only Memory or Plash Memory. The memory or computer readable medium used in the invention can be contained within a single computer or distributed in a network. A network can be any of a number of conventional network systems known in the art such as a local area network (LAN) or a wide area network (WAN). Client-server environments, database servers and networks that can be used in the invention are well known in the art. For example, the database server can run on an operating system such as UNIX™, running a relational database management system, a World Wide Web application and a World Wide Web server. Other types of memories and computer readable media are also contemplated to function within the scope of the invention.

A database or data structure of the invention can be represented in a markup language format including, for example, Standard Generalized Markup Language (SGML), Hypertext markup language (HTML) or Extensible Markup language (XML). Markup languages can be used to tag the information stored in a database or data structure of the invention, thereby providing convenient annotation and transfer of data between databases and data structures. In particular, an XML format can be useful for structuring the data representation of reactions, reactants and their annotations; for exchanging database contents, for example, over a network or internet; for updating individual elements using the document object model; or for providing differential access to multiple users for different information content of a data base or data structure of the invention. XML programming methods and editors for writing XML code are known in the art as described, for example, in Ray, Learning XML O'Reilly and Associates, Sebastopol, Calif. (2001).

A set of constraints can be applied to a reaction network data structure to simulate the flux of mass through the reaction network under a particular set of environmental conditions specified by a constraints set. Because the time constants characterizing metabolic transients and/or metabolic reactions are typically very rapid, on the order of milli-seconds to seconds, compared to the time constants of cell growth on the order of hours to days, the transient mass balances can be simplified to only consider the steady state behavior. Referring now to an example where the reaction network data structure is a stoichiometric matrix, the steady state mass balances can be applied using the following system of linear equations S·ν=0  (Eq.2) where S is the stoichiometric matrix as defined above and ν is the flux vector. This equation defines the mass, energy, and redox potential constraints placed on the metabolic network as a result of stoichiometry. Together Equations 1 and 2 representing the reaction constraints and mass balances, respectively, effectively define the capabilities and constraints of the metabolic genotype and the organism's metabolic potential. All vectors, ν, that satisfy Equation 2 are said to occur in the mathematical nullspace of S. Thus, the null space defines steady-state metabolic flux distributions that do not violate the mass, energy, or redox balance constraints. Typically, the number of fluxes is greater than the number of mass balance constraints, thus a plurality of flux distributions satisfy the mass balance constraints and occupy the null space. The null space, which defines the feasible set of metabolic flux distributions, is further reduced in size by applying the reaction constraints set forth in Equation 1 leading to a defined solution space. A point in this space represents a flux distribution and hence a metabolic phenotype for the network. An optimal solution within the set of all solutions can be determined using mathematical optimization methods when provided with a stated objective and a constraint set. The calculation of any solution constitutes a simulation of the model.

Objectives for activity of S. cerevisiae can be chosen to explore the improved use of the metabolic network within a given reaction network data structure. These objectives can be design objectives for a strain, exploitation of the metabolic capabilities of a genotype, or physiologically meaningful objective functions, such as maximum cellular growth. Growth can be defined in terms of biosynthetic requirements based on literature values of biomass composition or experimentally determined values such as those obtained as described above. Thus, biomass generation can be defined as an exchange reaction that removes intermediate metabolites in the appropriate ratios and represented as an objective function. In addition to draining intermediate metabolites this reaction flux can be formed to utilize energy molecules such as ATP, NADH and NADPH so as to incorporate any growth dependent maintenance requirement that must be met. This new reaction flux then becomes another constraint/balance equation that the system must satisfy as the objective function. Using the stoichiometric matrix of FIG. 2 as an example, adding such a constraint is analogous to adding the additional column V_(growth) to the stoichiometric matrix to represent fluxes to describe the production demands placed on the metabolic system. Setting this new flux as the objective function and asking the system to maximize the value of this flux for a given set of constraints on all the other fluxes is then a method to simulate the growth of the organism.

Continuing with the example of the stoichiometric matrix applying a constraint set to a reaction network data structure can be illustrated as follows. The solution to equation 2 can be formulated as an optimization problem, in which the flux distribution that minimizes a particular objective is found. Mathematically, this optimization problem can be stated as: Minimize Z  (Eq. 3) where z=Σc ₁·ν₁  (Eq. 4) where Z is the objective which is represented as a linear combination of metabolic fluxes ν_(i) using the weights c_(i) in this linear combination. The optimization problem can also be stated as the equivalent maximization problem; i.e. by changing the sign on Z. Any commands for solving the optimization problem can be used including, for example, linear programming commands.

A computer system of the invention can further include a user interface capable of receiving a representation of one or more reactions. A user interface of the invention can also be capable of sending at least one command for modifying the data structure, the constraint set or the commands for applying the constraint set to the data representation, or a combination thereof. The interface can be a graphic user interface having graphical means for making selections such as menus or dialog boxes. The interface can be arranged with layered screens accessible by making selections from a main screen. The user interface can provide access to other databases useful in the invention such as a metabolic reaction database or links to other databases having information relevant to the reactions or reactants in the reaction network data structure or to S. cerevisiae physiology. Also, the user interface can display a graphical representation of a reaction network or the results of a simulation using a model of the invention.

Once an initial reaction network data structure and set of constraints has been created, this model can be tested by preliminary simulation. During preliminary simulation, gaps in the network or “dead-ends” in which a metabolite can be produced but not consumed or where a metabolite can be consumed but not produced can be identified. Based on the results of preliminary simulations areas of the metabolic reconstruction that require an additional reaction can be identified. The determination of these gaps can be readily calculated through appropriate queries of the reaction network data structure and need not require the use of simulation strategies, however, simulation would be an alternative approach to locating such gaps.

In the preliminary simulation testing and model content refinement stage the existing model is subjected to a series of functional tests to determine if it can perform basic requirements such as the ability to produce the required biomass constituents and generate predictions concerning the basic physiological characteristics of the particular organism strain being modeled. The more preliminary testing that is conducted the higher the quality of the model that will be generated. Typically the majority of the simulations used in this stage of development will be single optimizations. A single optimization can be used to calculate a single flux distribution demonstrating how metabolic resources are routed determined from the solution to one optimization problem. An optimization problem can be solved using linear programming as demonstrated in the Examples below. The result can be viewed as a display of a flux distribution on a reaction map. Temporary reactions can be added to the network to determine if they should be included into the model based on modeling/simulation requirements.

Once a model of the invention is sufficiently complete with respect to the content of the reaction network data structure according to the criteria set forth above, the model can be used to simulate activity of one or more reactions in a reaction network. The results of a simulation can be displayed in a variety of formats including, for example, a table, graph, reaction network, flux distribution map or a phenotypic phase plane graph.

Thus, the invention provides a method for predicting a S. cerevisiae physiological function. The method includes the steps of (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function.

As used herein, the term “physiological function,” when used in reference to S. cerevisiae, is intended to mean an activity of a S. cerevisiae cell as a whole. An activity included in the term can be the magnitude or rate of a change from an initial state of a S. cerevisiae cell to a final state of the S. cerevisiae cell. An activity can be measured qualitatively or quantitatively. An activity included in the term can be, for example, growth, energy production, redox equivalent production, biomass production, development, or consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen. An activity can also be an output of a particular reaction that is determined or predicted in the context of substantially all of the reactions that affect the particular reaction in a S. cerevisiae cell or substantially all of the reactions that occur in a S. cerevisiae cell. Examples of a particular reaction included in the term are production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, or transport of a metabolite. A physiological function can include an emergent property which emerges from the whole but not from the sum of parts where the parts are observed in isolation (see for example, Palsson Nat. Biotech 18:1147-1150 (2000)).

A physiological function of S. cerevisiae reactions can be determined using phase plane analysis of flux distributions. Phase planes are representations of the feasible set which can be presented in two or three dimensions. As an example, two parameters that describe the growth conditions such as substrate and oxygen uptake rates can be defined as two axes of a two-dimensional space. The optimal flux distribution can be calculated from a reaction network data structure and a set of constraints as set forth above for all points in this plane by repeatedly solving the linear programming problem while adjusting the exchange fluxes defining the two-dimensional space. A finite number of qualitatively different metabolic pathway utilization patterns can be identified in such a plane, and lines can be drawn to demarcate these regions. The demarcations defining the regions can be determined using shadow prices of linear optimization as described, for example in Chvatal, Linear Programming New York, W.H. Freeman and Co. (1983). The regions are referred to as regions of constant shadow price structure. The shadow prices define the intrinsic value of each reactant toward the objective function as a number that is either negative, zero, or positive and are graphed according to the uptake rates represented by the x and y axes. When the shadow prices become zero as the value of the uptake rates are changed there is a qualitative shift in the optimal reaction network.

One demarcation line in the phenotype phase plane is defined as the line of optimality (LO). This line represents the optimal relation between respective metabolic fluxes. The LO can be identified by varying the x-axis flux and calculating the optimal y-axis flux with the objective function defined as the growth flux. From the phenotype phase plane analysis the conditions under which a desired activity is optimal can be determined. The maximal uptake rates lead to the definition of a finite area of the plot that is the predicted outcome of a reaction network within the environmental conditions represented by the constraint set. Similar analyses can be performed in multiple dimensions where each dimension on the plot corresponds to a different uptake rate. These and other methods for using phase plane analysis, such as those described in Edwards et al., Biotech Bioeng. 77:27-36(2002), can be used to analyze the results of a simulation using an in silico S. cerevisiae model of the invention.

A physiological function of S. cerevisiae can also be determined using a reaction map to display a flux distribution. A reaction map of S. cerevisiae can be used to view reaction networks at a variety of levels. In the case of a cellular metabolic reaction network a reaction map can contain the entire reaction complement representing a global perspective. Alternatively, a reaction map can focus on a particular region of metabolism such as a region corresponding to a reaction subsystem described above or even on an individual pathway or reaction. An example of a reaction map showing a subset of reactions in a reaction network of S. cerevisiae is shown in FIG. 4.

The invention also provides an apparatus that produces a representation of a S. cerevisiae physiological function, wherein the representation is produced by a process including the steps of: (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function; (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function, and (e) producing a representation of the activity of the one or more S. cerevisiae reactions.

The methods of the invention can be used to determine the activity of a plurality of S. cerevisiae reactions including, for example, biosynthesis of an amino acid, degradation of an amino acid, biosynthesis of a purine, biosynthesis of a pyrimidine, biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis of a cofactor, transport of a metabolite and metabolism of an alternative carbon source. In addition, the methods can be used to determine the activity of one or more of the reactions described above or listed in Table 2.

The methods of the invention can be used to determine a phenotype of a S. cerevisiae mutant. The activity of one or more S. cerevisiae reactions can be determined using the methods described above, wherein the reaction network data structure lacks one or more gene-associated reactions that occur in S. cerevisiae. Alternatively, the methods can be used to determine the activity of one or more S. cerevisiae reactions when a reaction that does not naturally occur in S. cerevisiae is added to the reaction network data structure. Deletion of a gene can also be represented in a model of the invention by constraining the flux through the reaction to zero, thereby allowing the reaction to remain within the data structure. Thus, simulations can be made to predict the effects of adding or removing genes to or from S. cerevisiae. The methods can be particularly useful for determining the effects of adding or deleting a gene that encodes for a gene product that performs a reaction in a peripheral metabolic pathway.

A drug target or target for any other agent that affects S. cerevisiae function can be predicted using the methods of the invention. Such predictions can be made by removing a reaction to simulate total inhibition or prevention by a drug or agent. Alternatively, partial inhibition or reduction in the activity a particular reaction can be predicted by performing the methods with altered constraints. For example, reduced activity can be introduced into a model of the invention by altering the α_(J) or β_(J) values for the metabolic flux vector of a target reaction to reflect a finite maximum or minimum flux value corresponding to the level of inhibition. Similarly, the effects of activating a reaction, by initiating or increasing the activity of the reaction, can be predicted by performing the methods with a reaction network data structure lacking a particular reaction or by altering the α_(j) or β_(J) values for the metabolic flux vector of a target reaction to reflect a maximum or minimum flux value corresponding to the level of activation. The methods can be particularly useful for identifying a target in a peripheral metabolic pathway.

Once a reaction has been identified for which activation or inhibition produces a desired effect on S. cerevisiae function, an enzyme or macromolecule that performs the reaction in S. cerevisiae or a gene that expresses the enzyme or macromolecule can be identified as a target for a drug or other agent. A candidate compound for a target identified by the methods of the invention can be isolated or synthesized using known methods. Such methods for isolating or synthesizing compounds can include, for example, rational design based on known properties of the target (see, for example, DeCamp et al., Protein Engineering Principles and Practice, Ed. Cleland and Craik, Wiley-Liss, New York, pp. 467-506 (1996)), screening the target against combinatorial libraries of compounds (see for example, Houghten et al., Nature, 354, 84-86 (1991); Dooley et al., Science, 266, 2019-2022 (1994), which describe an iterative approach, or R. Houghten et al. PCT/US91/08694 and U.S. Pat. No. 5,556,762 which describe a positional-scanning approach), or a combination of both to obtain focused libraries. Those skilled in the art will know or will be able to routinely determine assay conditions to be used in a screen based on properties of the target or activity assays known in the art.

A candidate drug or agent, whether identified by the methods described above or by other methods known in the art, can be validated using an in silico S. cerevisiae model or method of the invention. The effect of a candidate drug or agent on S. cerevisiae physiological function can be predicted based on the activity for a target in the presence of the candidate drug or agent measured in vitro or in vivo. This activity can be represented in an in silico S. cerevisiae model by adding a reaction to the model, removing a reaction from the model or adjusting a constraint for a reaction in the model to reflect the measured effect of the candidate drug or agent on the activity of the reaction. By running a simulation under these conditions the holistic effect of the candidate drug or agent on S. cerevisiae physiological function can be predicted.

The methods of the invention can be used to determine the effects of one or more environmental components or conditions on an activity of S. cerevisiae. As set forth above, an exchange reaction can be added to a reaction network data structure corresponding to uptake of an environmental component, release of a component to the environment, or other environmental demand. The effect of the environmental component or condition can be further investigated by running simulations with adjusted α_(j) or β_(j) values for the metabolic flux vector of the exchange reaction target reaction to reflect a finite maximum or minimum flux value corresponding to the effect of the environmental component or condition. The environmental component can be, for example an alternative carbon source or a metabolite that when added to the environment of S. cerevisiae can be taken up and metabolized. The environmental component can also be a combination of components present for example in a minimal medium composition. Thus, the methods can be used to determine an optimal or minimal medium composition that is capable of supporting a particular activity of S. cerevisiae.

The invention further provides a method for determining a set of environmental components to achieve a desired activity for S. cerevisiae. The method includes the steps of (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) applying the constraint set to the data representation, thereby determining the activity of one or more S. cerevisiae reactions (d) determining the activity of one or more S. cerevisiae reactions according to steps (a) through (c), wherein the constraint set includes an upper or lower bound on the amount of an environmental component and (e) repeating steps (a) through (c) with a changed constraint set, wherein the activity determined in step (e) is improved compared to the activity determined in step (d).

The following examples are intended to illustrate but not limit the present invention.

Example I Reconstruction of the Metabolic Network of S. Cerevisiae

This example shows how the metabolic network of S. cerevisiae can be reconstructed.

The reconstruction process was based on a comprehensive search of the current knowledge of metabolism in S. cerevisiae as shown in FIG. 5. A reaction database was built using the available genomic and metabolic information on the presence, reversibility, localization and cofactor requirements of all known reactions. Furthermore, information on non-growth-dependent and growth-dependent ATP requirements and on the biomass composition was used.

For this purpose different online reaction databases, recent publications and review papers (Table 5 and 9), and established biochemistry textbooks (Zubay, Biochemistry Wm.C. Brown Publishers, Dubuque, Iowa (1998); Stryer, Biochemistry W.H. Freeman, New York, N.Y. (1988)) were consulted. Information on housekeeping genes of S. cerevisiae and their functions were taken from three main yeast on-line resources:

-   -   The MIPS Comprehensive Yeast Genome Database (CYGD) (Mewes et         al., Nucleic Acids Research 30(1): 31-34 (2002));     -   The Saccharomyces Genome Database (SGD) (Cherry et al., Nucleic         Acids Research 26(1): 73-9 (1998));     -   The Yeast Proteome Database (YPD) (Costanzo et al., Nucleic         Acids Research 29(1): 75-9 (2001)).

The following metabolic maps and protein databases (available online) were investigated:

-   -   Kyoto Encyclopedia of Genes and Genomes database (KEGG)         (Kanehisa et al., Nucleic Acids Research 28(1): 27-30 (2000));     -   The Biochemical Pathways database of the Expert Protein Analysis         System database (ExPASy) (Appel et al., Trends Biochem Sci.         19(6): 258-260 (1994));     -   ERGO from Integrated Genomics (www.integratedgenomics.com)     -   SWISS-PROT Protein Sequence database (Bairoch et al., Nucleic         Acids Research 28(1): 45-48 (2000)).

Table 5 lists additional key references that were consulted for the reconstruction of the metabolic network of S. cerevisiae.

TABLE 5 Amino acid biosynthesis Strathern et al., The Molecular biology of the yeast Saccharomyces: metabolism and gene expression Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1982)) Lipid synthesis Daum et al., Yeast 14(16): 1471-510 (1998); Dickinson et al., The metabolism and molecular physiology of Saccharomyces cerevisiae Taylor & Francis, London; Philadelphia (1999); Dickson et al., Methods Enzymol. 311: 3-9 (2000); Dickson, Annu Rev Biochem 67: 27-48 (1998); Parks, CRC Crit Rev Microbiol 6(4): 301-41 (1978)) Nucleotide Metabolism Strathern et al., supara (1982)) Oxidative phosphorylation and electron transport (Verduyn et al., Antonie Van Leeuwenhoek 59(1): 49-63 (1991); Overkamp et al., J. of Bacteriol 182(10): 2823-2830 (2000)) Primary Metabolism Zimmerman et al., Yeast sugar metabolism: biochemistry, genetics, biotechnology, and applications Technomic Pub., Lancaster, PA (1997); Dickinson et al., supra (1999); Strathern et al., supra (1982)) Transport across the cytoplasmic membrane Paulsen et al., FEBS Lett 430(1-2): 116-125 (1998); Wieczorke et al., FEBS Lett 464(3): 123-128 (1999); Regenberg et al., Curr Genet 36(6): 317-328 (1999); Andre, Yeast 11(16): 1575-1611 (1995)) Transport across the mitochondrial membrane Palmieri et al., J. Bioenerg Biomembr 32(1): 67:77 (2000); Palmieri et al., Biochim Biophys Acta 1459(2-3): 363-369 (2000); Palmieri et al., J. Biol Chem 274(32): 22184-22190 (1999); Palmieri et al., FEBS Lett 417(1): 114-118 (1997); Paulsen et al., supra (1998); Pallotta et al., FEBS Lett 428(3): 245-249 (1998); Tzagologg et al. Mitochondria Plenum Press, New York (1982); Andre Yeast 11(16): 1575-611 (1995))

All reactions are localized into the two main compartments, cytosol and mitochondria, as most of the common metabolic reactions in S. cerevisiae take place in these compartments. Optionally, one or more additional compartments can be considered. Reactions located in vivo in other compartments or reactions for which no information was available regarding localization were assumed to be cytosol. All corresponding metabolites were assigned appropriate localization and a link between cytosol and mitochondria was established through either known transport and shuttle systems or through inferred reactions to meet metabolic demands.

After the initial assembly of all the metabolic reactions the list was manually examined for resolution of detailed biochemical issues. A large number of reactions involve cofactors utilization, and for many of these reactions the cofactor requirements have not yet been completely elucidated. For example, it is not clear whether certain reactions use only NADH or only NADPH as a cofactor or can use both cofactors, whereas other reactions are known to use both cofactors. For example, a mitochondrial aldehyde dehydrogenase encoded by ALD4 can use both NADH and NADPH as a cofactor (Remize et al. Appl Environ Microbiol 66(8): 3151-3159 (2000)). In such cases, two reactions are included in the reconstructed metabolic network.

Further considerations were taken into account to preserve the unique features of S. cerevisiae metabolism. S. cerevisiae lacks a gene that encodes the enzyme transhydrogenase. Insertion of a corresponding gene from Azetobacter vinelandii in S. cerevisiae has a major impact on its phenotypic behavior, especially under anaerobic conditions (Niessen et al. Yeast 18(1): 19-32 (2001)). As a result, reactions that create a net transhydrogenic effect in the model were either constrained to zero or forced to become irreversible. For instance, the flux carried by NADH dependent glutamate dehydrogenase (Gdh2p) was constrained to zero to avoid the appearance of a net transhydrogenase activity through coupling with the NADPH dependent glutamate dehydrogenases (Gdh1p and Gdh3p).

Once a first generation model is prepared, microbial behavior can be modeled for a specific scenario, such as anaerobic or aerobic growth in continuous cultivation using glucose as a sole carbon source. Modeling results can then be compared to experimental results. If modeling and experimental results are in agreement, the model can be considered as correct, and it is used for further modeling and predicting S. cerevisiae behavior. If the modeling and experimental results are not in agreement, the model has to be evaluated and the reconstruction process refined to determine missing or incorrect reactions, until modeling and experimental results are in agreement. This iterative process is shown in FIG. 5 and exemplified below.

Example II Calculation of the P/O Ratio

This example shows how the genome-scale reconstructed metabolic model of S. cerevisiae was used to calculate the P/O ratio, which measures the efficiency of aerobic respiration. The P/O ratio is the number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS).

Linear optimization was applied, and the in silico P/O ratio was calculated by first determining the maximum number of ATP molecules produced per molecule of glucose through the electron transport system (ETS), and then interpolating the in silico P/O ratio using the theoretical relation (i.e. in S. cerevisiae for the P/O ratio of 1.5, 18 ATP molecules are produced).

Experimental studies of isolated mitochondria have shown that S. cerevisiae lacks site I proton translocation (Verduyn et al., Antonie Van Leeuwenhoek 59(1): 49-63 (1991)). Consequently, estimation of the maximum theoretical or “mechanistic” yield of the ETS alone gives a P/O ratio of 1.5 for oxidation of NADH in S. cerevisiae grown on glucose (Verduyn et al., supra (1991)). However, based on experimental measurements, it has been determined that the net in vivo P/O ratio is approximately 0.95 (Verduyn et al., supra (1991)). This difference is generally thought to be due to the use of the mitochondrial transmembrane proton gradient needed to drive metabolite exchange, such as the proton-coupled translocation of pyruvate, across the inner mitochondrial membrane. Although simple diffusion of protons (or proton leakage) would be surprising given the low solubility of protons in the lipid bilayer, proton leakage is considered to contribute to the lowered P/O ratio due to the relatively high electrochemical gradient across the inner mitochondrial membrane (Westerhoff and van Dam, Thermodynamics and control of biological free-energy transduction Elsevier, New York, N.Y. (1987)).

Using the reconstructed network, the P/O ratio was calculated to be 1.04 for oxidation of NADH for growth on glucose by first using the model to determine the maximum number of ATP molecules produced per molecule of glucose through the electron transport system (ETS) (YATP,max=12.5 ATP molecules/glucose molecule via ETS in silico). The in silico P/O ratio was then interpolated using the theoretical relation (i.e. 18 ATP molecules per glucose molecule are produced theoretically when the P/O ratio is 1.5). The calculated P/O ratio was found to be close to the experimentally determined value of 0.95. Proton leakage, however, was not included in the model, which suggests that the major reason for the lowered P/O ratio is the use of the proton gradient for solute transport across the inner mitochondrial membrane. This result illustrates the importance of including the complete metabolic network in the analysis, as the use of the proton gradient for solute transport across the mitochondrial membrane contributes significantly to the operational P/O ratio.

Example III Phenotypic Phase Plane Analysis

This example shows how the S. cerevisiae metabolic model can be used to calculate the range of characteristic phenotypes that the organism can display as a function of variations in the activity of multiple reactions.

For this analysis, O₂ and glucose uptake rates were defined as the two axes of the two-dimensional space. The optimal flux distribution was calculated using linear programming (LP) for all points in this plane by repeatedly solving the LP problem while adjusting the exchange fluxes defining the two-dimensional space. A finite number of quantitatively different metabolic pathway utilization patterns were identified in the plane, and lines were drawn to demarcate these regions. One demarcation line in the phenotypic phase plane (PhPP) was defined as the line of optimality (LO), and represents the optimal relation between the respective metabolic fluxes. The LO was identified by varying the x-axis (glucose uptake rate) and calculating the optimal y-axis (O₂ uptake rate), with the objective function defined as the growth flux. Further details regarding Phase-Plane Analysis are provided in Edwards et al., Biotechnol. Bioeng. 77:27-36 (2002) and Edwards et al., Nature Biotech. 19:125-130 (2001)).

As illustrated in FIG. 6, the S. cerevisiae PhPP contains 8 distinct metabolic phenotypes. Each region (P1-P8) exhibits unique metabolic pathway utilization that can be summarized as follows:

The left-most region is the so-called “infeasible” steady state region in the PhPP, due to stoichiometric limitations.

From left to right:

P1: Growth is completely aerobic. Sufficient oxygen is available to complete the oxidative metabolism of glucose to support growth requirements. This zone represents a futile cycle. Only CO₂ is formed as a metabolic by-product. The growth rate is less than the optimal growth rate in region P2. The P1 upper limit represents the locus of points for which the carbon is completely oxidized to eliminate the excess electron acceptor, and thus no biomass can be generated.

P2: Oxygen is slightly limited, and all biosynthetic cofactor requirements cannot be optimally satisfied by oxidative metabolism. Acetate is formed as a metabolic by-product enabling additional high-energy phosphate bonds via substrate level phosphorylation. With the increase of O₂ supply, acetate formation eventually decreases to zero.

P3: Acetate is increased and pyruvate is decreased with increase in oxygen uptake rate.

P4: Pyruvate starts to increase and acetate is decreased with increase in oxygen uptake rate. Ethanol production eventually decreases to zero.

P5: The fluxes towards acetate formation are increasing and ethanol production is decreasing.

P6: When the oxygen supply increases, acetate formation increases and ethanol production decreases with the carbon directed toward the production of acetate. Besides succinate production, malate may also be produced as metabolic by-product.

P7: The oxygen supply is extremely low, ethanol production is high and succinate production is decreased. Acetate is produced at a relatively low level.

P8: This region is along the Y-axis and the oxygen supply is zero. This region represents completely anaerobic fermentation. Ethanol and glycerol are secreted as a metabolic by-product. The role of NADH-consuming glycerol formation is to maintain the cytosol redox balance under anaerobic conditions (Van Dijken and Scheffers Yeast 2(2): 123-7 (1986)).

Line of Optimality: Metabolically, the line of optimality (LO) represents the optimal utilization of the metabolic pathways without limitations on the availability of the substrates. On an oxygen/glucose phenotypic phase plane diagram, LO represents the optimal aerobic glucose-limited growth of S. cerevisiae metabolic network to produce biomass from unlimited oxygen supply for the complete oxidation of the substrates in the cultivation processes. The line of optimality therefore represents a completely respiratory metabolism, with no fermentation by-product secretion and the futile cycle fluxes equals zero.

Thus, this example demonstrates that Phase Plane Analysis can be used to determine the optimal fermentation pattern for S. cerevisiae, and to determine the types of organic byproducts that are accumulated under different oxygenation conditions and glucose uptake rates.

Example IV Calculation of Line of Optimality and Respiratory Quotient

This example shows how the S. cerevisiae metabolic model can be used to calculate the oxygen uptake rate (OUR), the carbon dioxide evolution rate (CER) and the respiration quotient (RQ), which is the ratio of CER over OUR.

The oxygen uptake rate (OUR) and the carbon dioxide evolution rate (CER) are direct indicators of the yeast metabolic activity during the fermentation processes. RQ is a key metabolic parameter that is independent of cell number. As illustrated in FIG. 7, if the S. cerevisiae is grown along the line of optimality, LO, its growth is at optimal aerobic rate with all the carbon sources being directed to biomass formation and there are no metabolic by-products secreted except CO₂. The calculated RQ along the LO is a constant value of 1.06; the RQ in P1 region is less than 1.06; and the RQ in the remaining regions in the yeast PhPP are greater than 1.06. The RQ has been used to determine the cell growth and metabolism and to control the glucose feeding for optimal biomass production for decades (Zeng et al. Biotechnol. Bioeng. 44:1107-1114 (1994)). Empirically, several researchers have proposed the values of 1.0 (Zigova, J Biotechnol 80: 55-62 (2000). Journal of Biotechnology), 1.04 (Wang et al., Biotechnol & Bioeng 19:69-86 (1977)) and 1.1 (Wang et al., Biotechnol. & Bioeng. 21:975-995 (1979)) as optimal RQ which should be maintained in fed-batch or continuous production of yeast's biomass so that the highest yeast biomass could be obtained (Dantigny et al., Appl. Microbiol. Biotechnol. 36:352-357 (1991)). The constant RQ along the line of optimality for yeast growth by the metabolic model is thus consistent with the empirical formulation of the RQ through on-line measurements from the fermentation industry.

Example V Computer Simulations

This example shows computer simulations for the change of metabolic phenotypes described by the yeast PHPP.

A piece-wise linearly increasing function was used with the oxygen supply rates varying from completely anaerobic to fully aerobic conditions (with increasing oxygen uptake rate from 0 to 20 mmol per g cell-hour). A glucose uptake rate of 5 mmol of glucose per g (dry weight)-hour was arbitrarily chosen for these computations. As shown in FIG. 8A, the biomass yield of the in silico S. cerevisiae strain was shown to increase from P8 to P2, and become optimal on the LO. The yield then started to slowly decline in P1 (futile cycle region). At the same time, the RQ value declines in relation to the increase of oxygen consumption rate, reaching a value of 1.06 on the LO1 and then further declining to become less than 1.

FIG. 8B shows the secretion rates of metabolic by-products; ethanol, succinate, pyruvate and acetate with the change of oxygen uptake rate from 0 to 20 mmol of oxygen per g (dry weight)-h. Each one of these by-products is secreted in a fundamentally different way in each region. As oxygen increases from 0 in P7, glycerol production (data not shown in this figure) decreases and ethanol production increases. Acetate and succinate are also secreted.

Example VI Modeling of Phenotypic Behavior in Chemostat Cultures

This example shows how the S. cerevisiae metabolic model can be used to predict optimal flux distributions that would optimize fermentation performance, such as specific product yield or productivity. In particular, this example shows how flux based analysis can be used to determine conditions that would minimize the glucose uptake rate of S. cerevisiae grown on glucose in a continuous culture under anaerobic and under aerobic conditions.

In a continuous culture, growth rate is equivalent to the dilution rate and is kept at a constant value. Calculations of the continuous culture of S. cerevisiae were performed by fixing the in silico growth rate to the experimentally determined dilution rate, and minimizing the glucose uptake rate. This formulation is equivalent to maximizing biomass production given a fixed glucose uptake value and was employed to simulate a continuous culture growth condition. Furthermore, a non growth dependent ATP maintenance of 1 mmol/gDW, a systemic P/O ratio of 1.5 (Verduyn et al. Antonie Van Leeuwenhoek 59(1): 49-63 (1991)), a polymerization cost of 23.92 mmol ATP/gDW, and a growth dependent ATP maintenance of 35.36 mmol ATP/gDW, which is simulated for a biomass yield of 0.51 gDW/h, are assumed. The sum of the latter two terms is included into the biomass equation of the genome-scale metabolic model.

Optimal growth properties of S. cerevisiae were calculated under anaerobic glucose-limited continuous culture at dilution rates varying between 0.1 and 0.4 h⁻¹. The computed by-product secretion rates were then compared to the experimental data (Nissen et al. Microbiology 143(1): 203-18 (1997)). The calculated uptake rates of glucose and the production of ethanol, glycerol, succinate, and biomass are in good agreement with the independently obtained experimental data (FIG. 9). The relatively low observed acetate and pyruvate secretion rates were not predicted by the iii silico model since the release of these metabolites does not improve the optimal solution of the network.

It is possible to constrain the in silico model further to secrete both, pyruvate and acetate at the experimental level and recompute an optimal solution under these additional constraints. This calculation resulted in values that are closer to the measured glucose uptake rates (FIG. 9A). This procedure is an example of an iterative data-driven constraint-based modeling approach, where the successive incorporation of experimental data is used to improve the in silico model. Besides the ability to describe the overall growth yield, the model allows further insight into how the metabolism operates. From further analysis of the metabolic fluxes at anaerobic growth conditions the flux through the glucose-6-phosphate dehydrogenase was found to be 5.32% of the glucose uptake rate at dilution rate of 0.1 h⁻¹, which is consistent with experimentally determined value (6.34%) for this flux when cells are operating with fermentative metabolism (Nissen et al., Microbiology 143(1): 203-218 (1997)).

Optimal growth properties of S. cerevisiae were also calculated under aerobic glucose-limited continuous culture in which the Crabtree effect plays an important role. The molecular mechanisms underlying the Crabtree effect in S. cerevisiae are not known. The regulatory features of the Crabtree effect (van Dijken et al. Antonie Van Leeuwenhoek 63(3-4):343-52 (1993)) can, however, be included in the in silico model as an experimentally determined growth rate-dependent maximum oxygen uptake rate (Overkamp et al. J. of Bacteriol 182(10): 2823-30 (2000))). With this additional constraint and by formulating growth in a chemostat as described above, the in silico model makes quantitative predictions about the respiratory quotient, glucose uptake, ethanol, CO2, and glycerol secretion rates under aerobic glucose-limited continuous condition (FIG. 10).

Example VII Analysis of Deletion of Genes Involved in Central Metabolism in S. Cerevsiae

This example shows how the S. cerevisiae metabolic model can be used to determine the effect of deletions of individual reactions in the network.

Gene deletions were performed in silico by constraining the flux(es) corresponding to a specific gene to zero. The impact of single gene deletions on growth was analysed by simulating growth on a synthetic complete medium containing glucose, amino acids, as well as purines and pyrimidines.

In silico results were compared to experimental results as supplied by the Saccharomyces Genome Database (SGD) (Cherry et al., Nucleic Acids Research 26(1):73-79 (1998)) and by the Comprehensive Yeast Genome Database (Mewes et al., Nucleic Acids Research 30(1):31-34 (2002)). In 85.6% of all considered cases (499 out of 583 cases), the in silico prediction was in qualitative agreement with experimental results. An evaluation of these results can be found in Example VIII. For central metabolism, growth was predicted under various experimental conditions and 81.5% (93 out of 114 cases) of the in silico predictions were in agreement with in vivo phenotypes.

Table 6 shows the impact of gene deletions on growth in S. cerevisiae. Growth on different media was considered, including defined complete medium with glucose as the carbon source, and minimal medium with glucose, ethanol or acetate as the carbon source. The complete reference citations for Table 6 can be found in Table 9.

Thus, this example demonstrates that the in silico model can be used to uncover essential genes to augment or circumvent traditional genetic studies.

TABLE 6 Defined Medium Complete Minimal Minimal Minimal Carbon Source Glucose Glucose Acetate Ethanol References: Gene in silico/in vivo in silico/in vivo in silico/in vivo in silico/in vivo (Minimal media) ACO1 +/+ −/− (Gangloff et al., 1990) CDC19# +/− +/− (Boles et al., 1998) CIT1 +/+ +/+ (Kim et al., 1986) CIT2 +/+ +/+ (Kim et al., 1986) CIT3 +/+ DAL7 +/+ +/+ +/+ +/+ (Hartig et al., 1992) ENO1 +/+ ENO2^($$) +/− +/− FBA1^(*) +/− +/− FBP1 +/+ +/+ +/− (Sedivy and Fraenkel, 1985; Gancedo and Delgado, 1984) FUM1 +/+ GLK1 +/+ GND1^(##) +/− +/− GND2 +/+ GPM1^(∥) +/− +/− GPM2 +/+ GPM3 +/+ HXK1 +/+ HXK2 +/+ ICL1 +/+ +/+ (Smith et al., 1996) IDH1 +/+ +/+ (Cupp and McAlister-Henn, 1992) IDH2 +/+ +/+ (Cupp and McAlister-Henn, 1992) IDP1 +/+ +/+ (Loftus et al., 1994) IDP2 +/+ +/+ (Loftus et al., 1994) IDP3 +/+ KGD1 +/+ +/+ (Repetto and Tzagoloff, 1991) KGD2 +/+ +/+ (Repetto and Tzagoloff, 1991) LPD1 +/+ LSC1 +/+ +/+ +/+ (Przybyla-Zawislak et al., 1998) LSC2 +/+ +/+ +/+ (Przybyla-Zawislak et al., 1998) MAE1 +/+ +/+ +/+ (Boles et al., 1998) MDH1 +/+ +/+ +/− (McAlister-Henn and Thompson, 1987) MDH2 +/+ +/+ +/+ +/− (McAlister-Henn and Thompson, 1987) MDH3 +/+ MLS1 +/+ +/+ +/+ +/+ (Hartig et al., 1992) OSM1 +/+ PCK1 +/+ PDC1 +/+ +/+ (Flikweert et al., 1996) PDC5 +/+ +/+ (Flikweert et al., 1996) PDC6 +/+ +/+ (Flikweert et al., 1996) PFK1 +/+ +/+ (Clifton and Fraenkel, 1982) PFK2 +/+ +/+ (Clifton and Fraenkel, 1982) PGI1*^(, &) +/− +/− (Clifton et al., 1978) PGK1* +/− +/− PGM1 +/+ +/+ (Boles et al., 1994) PGM2 +/+ +/+ (Boles et al., 1994) PYC1 +/+ +/+ +/− +/− (Wills and Melham, 1985) PYC2 +/+ PYK2 +/+ +/+ +/+ (Boles et al., 1998; McAlister- Henn and Thompson, 1987) RK11 −/− RPE1 +/+ SOL1 +/+ SOL2 +/+ SOL3 +/+ SOL4 +/+ TAL1 +/+ +/+ (Schaaff-Gerstenschläger and Zimmermann, 1993) TDH1 +/+ TDH2 +/+ TDH3 +/+ TKL1 +/+ +/+ (Schaff-Gerstenschläger and Zimmermann, 1993) TKL2 +/+ TPI1*^(, $) +/− ZWF1 +/+ +/+ (Schaaff-Gerstenschläger and Zimmermann, 1993) +/− Growth/no growth ^(#)The isoenyzme Pyk2p is glucose repressed, and cannot sustain growth on glucose. *Model predicts single deletion mutant to be (highly) growth retarded. ^($)Growth of single deletion mutant is inhibited by glucose. ^(&)Different hypotheses exist for why Pgilp deficient mutants do not grow on glucose, e.g. the pentose phosphate pathway in S. cerevisiae is insufficient to support growth and cannot supply the EMP pathway with sufficient amounts of fructose-6-phosphate and glyceraldehydes-3-phosphate (Boles, 1997). ^(∥)The isoenzymes Gpm2p and Gpm3p cannot sustain growth on glucose. They only show residual in vivo activity when they are expressed from a foreign promoter (Heinisch et al., 1998). ^(##)Gndlp accounts for 80% of the enzyme activity. A mutant deleted in GND1 accumulates gluconate-6-phosphate, which is toxic to the cell (Schaaff-Gerstenschläger and Miosga, 1997). ^($$)ENO1 plays central role in gluconeogenesis whereas ENO2 is used in glycolysis (Müller and Entian, 1997).

Example VIII Large-Scale Gene Deletion Analysis in S. Cerevisiae

A large-scale in silico evaluation of gene deletions in S. cerevisiae was conducted using the genome-scale metabolic model. The effect of 599 single gene deletions on cell viability was simulated in silico and compared to published experimental results. In 526 cases (87.8%), the in silico results were in agreement with experimental observations when growth on synthetic complete medium was simulated. Viable phenotypes were predicted in 89.4% (496 out of 555) and lethal phenotypes are correctly predicted in 68.2% (30 out of 44) of the cases considered.

The failure modes were analyzed on a case-by-case basis for four possible inadequacies of the in silico model: 1) incomplete media composition; 2) substitutable biomass components; 3) incomplete biochemical information; and 4) missing regulation. This analysis eliminated a number of false predictions and suggested a number of experimentally testable hypotheses. The genome-scale in silico model of S. cerevisiae can thus be used to systematically reconcile existing data and fill in knowledge gaps about the organism.

Growth on complete medium was simulated under aerobic condition. Since the composition of a complete medium is usually not known in detail, a synthetic complete medium containing glucose, twenty amino acids (alanine, arginine, asparagine, aspartate, cysteine, glutamine, glutamate, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophane, tyrosine, valine) and purines (adenine and guanine) as well as pyrimidines (cytosine and thymine) was defined for modeling purposes. Furthermore, ammonia, phosphate, and sulphate were supplied. The in silico results were initially compared to experimental data from a competitive growth assay (Winzeler et al., Science 285:901-906 (1999)) and to available data from the MIPS and SGD databases (Mewes et al., Nucleic Acids Research 30(1):31-34 (2002); Cherry et al., Nucleic Acids Research 26(1):73-79 (1998)). Gene deletions were simulated by constraining the flux through the corresponding reactions to zero and optimizing for growth as previously described (Edwards and Palsson, Proceedings of the National Academy of Sciences 97(10):5528-5533 (2000)). For this analysis, a viable phenotype was defined as a strain that is able to meet all the defined biomass requirements and thus grow. Single gene deletion mutants that have a reduced growth rate compared to the wild type simulation are referred to as growth retarded mutants.

The analysis of experimental data was approached in three steps:

-   -   The initial simulation using the synthetic medium described         above, referred to as simulation 1.     -   False predictions of simulation 1 were subsequently examined to         determine if the failure was due to incomplete information in         the in silico model, such as missing reactions, the         reversibility of reactions, regulatory events, and missing         substrates in the synthetic complete medium. In simulation 2,         any such additional information was introduced into the in         silico model and growth was re-simulated for gene deletion         mutants whose in silico phenotype was not in agreement with its         in vivo phenotype.     -   A third simulation was carried out, in which dead end pathways         (i.e. pathways leading to intracellular metabolites that were         not further connected into the overall network), were excluded         from the analysis (simulation 3).

The effect of single gene deletions on the viability of S. cerevisiae was investigated for each of the 599 single gene deletion mutants. The in silico results were categorized into four groups:

1. True negatives (correctly predicted lethal phenotype);

2. False negatives (wrongly predicted lethal phenotype);

3. True positives (correctly predicted viable phenotypes);

4. False positives (wrongly predicted viable phenotypes).

In simulation 1, 509 out of 599 (85%) simulated phenotypes were in agreement with experimental data. The number of growth retarding genes in simulation 1 was counted to be 19, a surprisingly low number. Only one deletion, the deletion of TPI1, had a severe impact on the growth rate. Experimentally, a deletion in TPI1 is lethal (Ciriacy and Breitenbach, J Bacteriol 139(1):152-60 (1979)). In silico, a tpi1 mutant could only sustain a specific growth rate of as low as 17% of the wild type. All other growth retarding deletions sustained approximately 99% of wild type growth, with the exception of a deletion of the mitochondrial ATPase that resulted in a specific growth rate of approximately 90% of wild type.

Predictions of simulation 1 were evaluated in a detailed manner on a case-by-case basis to determine whether the false predictions could be explained by:

1. Medium composition used for the simulation;

2. The biomass composition used in the simulation;

3. Incomplete biochemical information; and

4. Effects of gene regulation.

Analysis of the false predictions from simulation 1 based on these possible failure modes resulted in model modifications that led to 526 out of 599 correctly predicted phenotypes (87.8%), i.e. simulation 2.

Simulation 3 uncovered some 220 reactions in the reconstructed network that are involved in dead end pathways. Removing these reactions and their corresponding genes from the genome-scale metabolic flux balance model, simulation 3 resulted in 473 out of 530 (89.6%) correctly predicted phenotypes of which 91.4% are true positive and 69.8% are true negative predictions.

Table 7 provides a summary of the large-scale evaluation of the effect of in silico single gene deletions in S. cerevisiae on viability.

TABLE 7 Genes involved in dead end Simulation 1 2 pathways 3 Number of deletion 599 599 530 Predicted Total 509 526 475 True positive 481 496 51 445 True negative 28 30 0 30 False positive 63 59 17 42 False negative 27 14 1 13 Overall Prediction 85.0% 87.8% 89.6% Positive Prediction 88.4% 89.4% 91.4% Negative Prediction 50.9% 68.2% 69.8%

A comprehensive list of all the genes used in the in silico deletion studies and results of the analysis are provided in Table 8. Table 8 is organized according to the categories true negative, false negative, true positive and false positive predictions. Genes highlighted in grey boxes, such as

corresponded initially to false predictions (simulation 1); however, evaluation of the false prediction and simulation 2 identified these cases as true predictions. ORFs or genes that are in an open box, such as

were excluded in simulation 3, as the corresponding reactions catalysed steps in dead end pathways.

TABLE 8 False Positive ACS2

BET2 CDC19 CDC21 CDC8 CYR1

DFR1

DUT1

ENO2 ERG10 ERG13 FAD1 FMN1 FOL1 FOL2 FOL3 GFA1 GPM1

HIP1

ILV3 ILV5

LCB1 LCB2 MSS4 NAT2 NCP1

PCM1 PET9

PMA1 PRO3 QNS1 QRI1 RER2 RIB5

STT4 THI80 TOR2 TPI TSC10 UGP1 URA6 YDR341C YGL245W False Negative ADE3 ADK1 CHO1 CHO2 DPP1 ERG3 ERG4 ERG5 ERG6 INM1 MET6 OPI3 PPT2 YNK1 True Negative ACC1 ADE13 CDS1 DPM1 ERG1 ERG7 ERG8 ERG9 ERG11 ERG12 ERG20 ERG25 ERG26 ERG27 FBA1 GLN1 GUK1 IDI1 IPP1 MVD1 PGI1 PGK1 PIS1 PMI40 PSA1 RKI1 SAH1 SEC53 TRR1 YDR531W True Positive AAC1 AAC3 AAH1 AAT1 AAT2 ABZ1 ACO1 ACS1 ADE1 ADE12 ADE16 ADE17 ADE2 ADE4 ADE5 ADE6 ADE7 ADE8 ADH1 ADH2 ADH3 ADH4 ADH5 ADK2 AGP1

AGP3 ALD2 ALD3 ALD4 ALD5 ALD6 ALP1 ASP1 ATH1 ATP1 BAP2 BAP3 BAT1 BAT2 BGL2

CAN1 CAR1 CAR2 CAT2 CDA1 CDA2 CDD1

CHA1 CHS1 CHS2 CHS3 CIT1 CIT2 CIT3 CKI1 COQ1 COQ2

COX1 COX10 CPA2

CRC1

CSG2 CTA1 CTP1 CTT1

CYS3 CYS4 DAK1 DAK2 DAL1 DAL2 DAL3 DAL4

DAL7 DCD1 DEG1 DIC1 DIP5 DLD1

DPL1 DUR1 DUR3 ECM17 ECM31 ECM40 ECT1

ENO1

ERG2 ERG24 ERR1 ERR2 EXG1 EXG2 FAA1 FAA2 FAA3 FAA4 FAB1 FAS1 FBP1 FBP26 FCY1 FCY2 FKS1 FKS3 FLX1

FRDS FUI1 FUM1 FUN63 FUR1 FUR4 GAD1 GAL1 GAL10 GAL2 GAL7 GAP1 GCV1 GCV2 GDH1 GDH2 GDH3 GLC3 GLK1

GLR1 GLT1 GLY1 GNA1 GND1 GND2 GNP1 GPD1 GPD2 GPH1 GPM2 GPM3 GPX1 GPX2 GSC2 GSH1 GSH2 GSY1 GSY2 GUA1 GUT1 GUT2

HIS1 HIS2 HIS3 HIS4 HIS5 HIS6 HIS7 HMG1 HMG2

HNM1 HOM2 HOM3 HOM6 HOR2 HPT1 HXK1 HXK2 HXT1 HXT10 HXT11 HXT13 HXT14 HXT15 HXT16 HXT17 HXT2 HXT3 HXT4 HXT5 HXT6 HXT7 HXT8 HXT9 HYR1 ICL1 ICL2 IDH1 IDP1 IDP2 IDP3 ILV1 ILV2 INO1

ITR1 ITR2 JEN1 KGD1 KRE2 KTR1 KTR2 KTR3 KTR4 KTR6 LCB3 LCB4 LCB5

LEU2 LEU4

LSC1 LSC2 LYP1 LYS1 LYS12 LYS2 LYS20 LYS21 LYS4 LYS9 MAE1 MAK3 MAL12 MAL31 MAL32 MDH1 MDH2 MDH3 MEL1 MEP1 MEP2 MEP3

MET10 MET12 MET13 MET14 MET16 MET17 MET2 MET22 MET3 MET7 MHT1 MIR1 MIS1 MLS1

MSR1

MTD1 MUP1 MUP3 NAT1 NDH1 NDH2 NDI1

NPT1 NTA1 NTH1 NTH2 OAC1 ODC1 ODC2 ORT1 OSM1 PAD1 PCK1

PDA1 PDC2 PDC5 PDC6 PDE1 PDE2 PDX3 PFK1 PFK2 PFK26 PFK27 PGM1 PGM2 PHA2 PHO8 PHO11 PHO84

PMA2 PMP1 PMP2 PMT1 PMT2 PMT3 PMT4 PMT5 PMT6

PNP1 POS5

PPA2 PRM4 PRM5 PRM6 PRO1 PRO2 PRS1 PRS2 PRS3 PRS4 PRS5

PSD2 PTR2 PUR5 PUS1 PUS2 PUS4 PUT1 PUT2 PUT4 PYC1 PYC2 PYK2 QPT1 RAM1 RBK1 RHR2 RIB1 RIB4 RIB7 RMA1 RNR1 RNR3 RPE1 SAM1 SAM2 SAM3 SAM4

SDH3 SER1 SER2 SER3 SER33 SFA1 SFC1 SHM1 SHM2 SLC1 SOL1 SOL2 SOL3 SOL4

SPE1 SPE2 SPE3 SPE4 SPR1 SRT1 STL1 SUC2 SUL1 SUL2 SUR1 SUR2 TAL1 TAT1 TAT2 TDH1 TDH2 TDH3 THI20 THI21 THI22 THI6 THI7 THM2 THM3 THR1 THR4 TKL1 TKL2 TOR1 TPS1 TPS2 TPS3

TRP1 TRP2 TRP3 TRP4 TRP5

TSL1 TYR1 UGA1 UGA4 URA1 URA2 URA3 URA4 URA5 URA7 URA8 URA10 URH1 URK1 UTR1 VAP1 VPS34 XPT1 YAT1 YSR3 YUR1 ZWF1

YBR006W YBR284W YDL100C YDR111C YEL041W YER053C YFL030W YFR055W YGR012W YGR043C YGR125W YGR287C YIL145C YIL167W YJL070C YJL200C YJL216C YJL218W YJR078W

YLR231C YLR328W

YMR293C

The following text describes the analysis of the initially false predictions of simulation 1 that were performed, leading to simulation 2 results.

Influence of Media Composition on Simulation Results:

A rather simple synthetic complete medium composition was chosen for simulation 1. The in silico medium contained only glucose, amino acids and nucleotides as the main components. However, complete media often used for experimental purposes, e.g. the YPD medium containing yeast extract and peptone, include many other components, which are usually unknown.

False negative predictions: The phenotype of the following deletion mutants: ecm1Δ, yil145cΔ, erg2 Δ, erg24 Δ, fas1 Δ, ura1 Δ, ura2 Δ, ura3 Δ and ura4 Δ were falsely predicted to be lethal in simulation 1. In simulation 2, an additional supplement of specific substrate could rescue a viable phenotype in silico and as the supplemented substrate may be assumed to be part of a complex medium, the predictions were counted as true positive predictions in simulation 2. For example, both Ecm1 and Yil145c are involved in pantothenate synthesis. Ecm1 catalyses the formation of dehydropantoate from 2-oxovalerate, whereas Yil145c catalyses the final step in pantothenate synthesis from β-alanine and panthoate. In vivo, ecm1 Δ, and yil145c a mutants require pantothenate for growth (White et al., J Biol Chem 276(14): 10794-10800 (2001)). By supplying pantothenate to the synthetic complete medium in silico, the model predicted a viable phenotype and the growth rate was similar to in silico wild type S. cerevisiae.

Similarly other false predictions could be traced to medium composition:

-   -   Mutants deleted in ERG2 or ERG24 are auxotroph for ergosterol         (Silve et al., Mol Cell Biol 16(6): 2719-2727 (1996); Bourot and         Karst, Gene 165(1): 97-102 (1995)). Simulating growth on a         synthetic complete medium supplemented with ergosterol allowed         the model to accurately predict viable phenotypes.     -   A deletion of FAS1 (fatty acid synthase) is lethal unless         appropriate amounts of fatty acids are provided, and by addition         of fatty acids to the medium, a viable phenotype was predicted.     -   Strains deleted in URA1, URA2, URA3, or URA4 are auxotroph for         uracil (Lacroute, J Bacteriol 95(3): 824-832 (1968)), and by         supplying uracil in the medium the model predicted growth.

The above cases were initially false negative predictions, and simulation 2 demonstrated that these cases were predicted as true positive by adjusting the medium composition.

False positive predictions: Simulation 1 also contained false positive predictions, which may be considered as true negatives or as true positives. Contrary to experimental results from a competitive growth assay (Winzeler et al., Science 285: 901-906 (1999)), mutants deleted in ADEJ3 are viable in vivo on a rich medium supplemented with low concentrations of adenine, but grow poorly (Guetsova et al., Genetics 147(2): 383-397 (1997)). Adenine was supplied in the in silico synthetic complete medium. By not supplying adenine, a lethal mutant was predicted. Therefore, this case was considered as a true negative prediction.

A similar case was the deletion of GLN1, which codes a glutamine synthase, the only pathway to produce glutamine from ammonia. Therefore, gln1Δ mutants are glutamine auxotroph (Mitchell, Genetics 111(2):243-58 (1985)). In a complex medium, glutamine is likely to be deaminated to glutamate, particularly during autoclaving. Complex media are therefore likely to contain only trace amounts of glutamine, and gln1Δ mutants are therefore not viable. However, in silico, glutamine was supplied in the complete synthetic medium and growth was predicted. By not supplying glutamine to the synthetic complete medium, the model predicted a lethal phenotype resulting in a true negative prediction.

Ilv3 and Ilv5 are both involved in branched amino acid metabolism. One may expect that a deletion of ILV3 or ILV5 could be rescued with the supply of the corresponding amino acids. For this, the model predicted growth. However, contradictory experimental data exists. In a competitive growth assay lethal phenotypes were reported. However, earlier experiments showed that ilv3Δ and ilv5Δ mutants could sustain growth when isoleucine and valine were supplemented to the medium, as for the complete synthetic medium. Hence, these two cases were considered to be true positive predictions.

Influence of the Definition of the Biomass Equation

The genome-scale metabolic model contains the growth requirements in the form of biomass composition. Growth is defined as a drain of building blocks, such as amino acids, lipids, nucleotides, carbohydrates, etc., to form biomass. The number of biomass components is 44 (see Table 1). These building blocks are essential for the formation of cellular components and they have been used as a fixed requirement for growth in the in silico simulations. Thus, each biomass component had to be produced by the metabolic network otherwise the organism could not grow in silico. In vivo, one often finds deletion mutants that are not able to produce the original biomass precursor or building block; however, other metabolites can replace these initial precursors or building blocks. Hence, for a number of strains a wrong phenotype was predicted in silico for this reason.

Phosphatidylcholine is synthesized by three methylation steps from phosphatidylethanolamine (Dickinson and Schweizer, The metabolism and molecular physiology of Saccharomyces cerevisiae Taylor & Francis, London; Philadelphia (1999)). The first step in the synthesis of phosphatidylcholine from phosphatidylethanolamine is catalyzed by a methyltransferase encoded by CHO2 and the latter two steps are catalyzed by phospholipid methyltransferase encoded by OPI3. Strains deleted in CHO2 or OPI3 are viable (Summers et al., Genetics 120(4): 909-922 (1988); Daum et al., Yeast 14(16): 1471-1510 (1998)); however, either null mutant accumulates mono- and dimethylated phosphatidylethanolamine under standard conditions and display greatly reduced levels of phosphatidylcholine (Daum et al., Yeast 15(7): 601-614 (1999)). Hence, phosphatidylethanolamine can replace phosphatidylcholine as a biomass component. In silico, phosphatidylcholine is required for the formation of biomass. One may further speculate on whether an alternative pathway for the synthesis of phosphatidylcholine is missing in the model, since Daum et al., supra (1999) detected small amounts of phosphatidylcholine in cho2Δ mutants. An alternative pathway, however, was not included in the in silico model.

Deletions in the ergosterol biosynthetic pathways of ERG3, ERG4, ERG5 or ERG6 lead in vivo to viable phenotypes. The former two strains accumulate ergosta-8, 22,24 (28)-trien-3-beta-ol (Bard et al., Lipids 12(8): 645-654 (1977); Zweytick et al., FEBS Lett 470(1): 83-87 (2000)), whereas the latter two accumulate ergosta-5,8-dien-3beta-ol (Hata et al., J Biochem (Tokyo) 94(2): 501-510 (1983)), or zymosterol and smaller amounts of cholesta-5,7,24-trien-3-beta-ol and cholesta-5,7,22,24-trien-3-beta-ol (Bard et al., supra (1977); Parks et al., Crit Rev Biochem Mol Biol 34(6): 399-404 (1999)), respectively, components that were not included in the biomass equations.

The deletion of the following three genes led to false positive predictions: RER2, SEC59 and QIR1. The former two are involved in glycoprotein synthesis and the latter is involved in chitin metabolism. Both chitin and glycoprotein are biomass components. However, for simplification, neither of the compounds was considered in the biomass equation. Inclusion of these compounds into the biomass equation may improve the prediction results.

Incomplete Biochemical Information

For a number of gene deletion mutants (inm1Δ, met6Δ, ynk1Δ, pho84Δ psd2Δ, tps2Δ), simulation 1 produced false predictions that could not be explained by any of the two reasons discussed above nor by missing gene regulation (see below). Further investigation of the metabolic network including an extended investigation of biochemical data from the published literature showed that some information was missing initially in the in silico model or information was simply not available.

Inm1 catalyses the ultimate step in inositol biosynthesis from inositol 1-phosphate to inositol (Murray and Greenberg, Mol Microbiol 36(3): 651-661 (2000)). Upon deleting INM1, the model predicted a lethal phenotype in contrary to the experimentally observed viable phenotype. An isoenzyme encoded by IMP2 was initially not included in the model, which may take over the function of INM1 and this addition would have led to a correct prediction. However, an inm1Δimp2Δ in vivo double deletion mutant is not inositol auxotroph (Lopez et al., Mol Microbiol 31(4): 1255-1264 (1999)). Hence, it appears that alternative routes for the production of inositol probably exist. Due to the lack of comprehensive biochemical knowledge, effects on inositol biosynthesis and the viability of strains deleted in inositol biosynthetic genes could not be explained.

Met6Δ mutants are methionine auxotroph (Thomas and Surdin-Kerjan, Microbiol Mol Biol Rev 61(4):503-532 (1997)), and growth may be sustained by the supply of methionine or S-adenosyl-L-methionine. In silico growth was supported neither by the addition of methionine nor by the addition of S-adenosyl-L-methionine. Investigation of the metabolic network showed that deleting MET6 corresponds to deleting the only possibility for using 5-methyltetrahydrofolate. Hence, the model appears to be missing certain information. A possibility may be that the carbon transfer is carried out using 5-methyltetrahydropteroyltri-L-glutamate instead of 5-methyltetrahydrofolate. A complete pathway for such a by-pass was not included in the genome-scale model.

The function of Ynk1p is the synthesis of nucleoside triphosphates from nucleoside diphosphates. YNK1Δ mutants have a 10-fold reduced Ynk1p activity (Fukuchi et al., Genes 129(1):141-146 (1993)), though this implies that there may either be an alternative route for the production of nucleoside triphosphates or a second nucleoside diphosphate kinase, even though there is no ORF in the genome with properties that indicates that there is a second nucleoside diphosphate kinase. An alternative route for the production of nucleoside triphosphate is currently unknown (Dickinson et al., supra (1999)), and was therefore not included in the model, hence a false negative prediction.

PHO84 codes for a high affinity phosphate transporter that was the only phosphate transporter included in the model. However, at least two other phosphate transporters exist, a second high affinity and Na⁺ dependent transporter Pho89 and a low affinity transporter (Persson et al., Biochim Biophys Acta 1422(3): 255-72 (1999)). Due to exclusion of these transporters a lethal pho84□ mutant was predicted. Including PHO89 and a third phosphate transporter, the model predicted a viable deletion mutant.

In a null mutant of PSD2, phosphatidylethanolamine synthesis from phosphatidylserine is at the location of Psd1 (Trotter et al., J Biol Chem 273(21): 13189-13196 (1998)), which is located in the mitochondria. It has been postulated that phosphatidylserine can be transported into the mitochondria and phosphatidylethanolamine can be transported out of the mitochondria. However, transport of phosphatidylethanolamine and phosphatidylserine over the mitochondrial membrane was initially not included in the model. Addition of these transporters to the genome-scale flux balance model allowed in silico growth of a PSD2 deleted mutant.

Strains deleted in TPS2 have been shown to be viable when grown on glucose (Bell et al., J Biol Chem 273(50): 33311-33319 (1998)). The reaction carried out by Tps2p was modeled as essential and as the final step in trehalose synthesis from trehalose 6-phosphate. However, the in vivo viable phenotype shows that other enzymes can take over the hydrolysis of trehalose 6-phosphate to trehalose from Tps2p (Bell et al., supra (1998)). The corresponding gene(s) are currently unknown. Inclusion of a second reaction catalyzing the final step of trehalose formation allowed for the simulation of a viable phenotype.

Strains deleted in ADE3 (C1-tetrahydrofolate synthase) and ADKI (Adenylate kinase) could not be readily explained. It is possible that alternative pathways or isoenzyme-coding genes for both functions exist among the many orphan genes still present in the S. cerevisiae.

The reconstruction process led to some incompletely modeled parts of metabolism. Hence, a number of false positive predictions may be the result of gaps (missing reactions) within pathways or between pathways, which prevent the reactions to completely connect to the overall pathway structure of the reconstructed model. Examples include:

-   -   Sphingolipid metabolism. It has not yet been fully elucidated         and therefore was not included completely into the model nor         were sphingolipids considered as building blocks in the biomass         equation.     -   Formation of tRNA. During the reconstruction process some genes         were included responsible for the synthesis of tRNA (DED81,         HTS1, KRS1, YDR41C, YGL245H).     -   However, pathways of tRNA synthesis were not fully included.     -   Heme synthesis was considered in the reconstructed model (HEM1,         HEM12, HEM13, HEM15, HEM2, HEM3, HEM4). However no reaction was         included that metabolized heme in the model.     -   Hence, the incomplete structure of metabolic network may be a         reason for false prediction of the phenotype of aur1Δ, Icb1Δ,         lcb2Δ, tsc10Δ, ded81Δ, hts1Δ, krs1Δ, ydr41Δ, yg1245wΔ, hem1Δ,         hem12Δ, hem13Δ, hem15Δ, hem2Δ, hem3Δ, and hem4Δ deletion         mutants. Reaction reversibility. The CHO1 gene encodes a         phosphatidylserine synthase, an integral membrane protein that         catalyses a central step in cellular phospholipid biosynthesis.         In vivo, a deletion in CHO1 is viable (Winzeler et al., Science         285: 901-906 (1999)). However, mutants are auxotrophic for         choline or ethanolamine on media containing glucose as the         carbon source (Bimer et al., Mol Biol Cell 12(4): 997-1007         (2001)).     -   Nevertheless, the model did not predict growth when choline         and/or ethanolamine were supplied. Further investigation of the         genome-scale model showed that this might be due to defining         reactions leading from phosphatidylserine to phosphatidylcholine         via phosphatidylethanolamine exclusively irreversible. By         allowing these reactions to be reversible, either supply of         choline and ethanolamine could sustain growth in silico.         Gene Regulation

Whereas many false negative predictions could be explained by either simulation of growth using the incorrect in silico synthetic complete medium or by initially missing information in the model, many false positives may be explained by in vivo catabolite expression, product inhibition effects or by repressed isoenzymes, as kinetic and other regulatory constraints were not included in the genome-scale metabolic model.

A total of 17 false positive predictions could be related to regulatory events. For a deletion of CDC19, ACS2 or ENO2 one may usually expect that the corresponding isoenzymes may take over the function of the deleted genes. However, the corresponding genes, either PYK2, ACS1 or ENO1, respectively, are subject to catabolite repression (Boles et al., J Bacteriol 179(9): 2987-2993 (1997); van den Berg and Steensma, Eur J Biochem 231(3): 704-713 (1995); Zimmerman et al., Yeast sugar metabolism: biochemistry, genetics, biotechnology, and applications Technomic Pub., Lancaster, Pa. (1997)). A deletion of GPM1 should be replaced by either of the two other isoenzymes, Gpm2 and Gpm3; however for the two latter corresponding gene products usually no activity is found (Heinisch et al., Yeast 14(3): 203-13 (1998)).

Falsely predicted growth phenotypes can often be explained when the corresponding deleted metabolic genes are involved in several other cell functions such as cell cycle, cell fate, communication, cell wall integrity, etc. The following genes whose deletions yielded false positive predictions were found to have functions other than just metabolic function: ACS2, BET2, CDCl9, CDC8, CYR1, DIM1, ENO2, FADl, GFA1, GPM1, HIP1, MSS4, PET9, PIK1, PMA1, STT4, TOR2. Indeed, a statistical analysis of the MIPS functional catalogue (found at the URL mips.gsf.de/proj/yeast/) showed that in general it was more likely to have a false prediction when the genes that had multiple functions were involved in cellular communication, cell cycling and DNA processing or control of cellular organization.

TABLE 9 Reference list for Table 2 Boles, E., Liebetrau, W., Hofmann, M. & Zimmermann, F. K. A family of hexosephosphate mutases in Saccharomyces cerevisiae. Eur. J. Biochem. 220, 83-96 (1994). Boles, E. Yeast sugar metabolism. Zimmermann, F. K. & Entian, K.-D. (eds.), pp. 81-96 (Technomic Publishing CO., INC., Lancaster, 1997). Boles, E., Jong-Gubbels, P. & Pronk, J. T. Identification and character- ization of MAE1, the Saccharomyces cerevisiae structural gene encoding mitochondrial malic enzyme. J. Bacteriol. 180, 2875-2882 (1998). Clifton, D., Weinstock, S. B. & Fraenkel, D. G. Glycolysis mutants in Saccharomyces cerevisiae. Genetics 88, 1-11 (1978). Clifton, D. & Fraenkel, D. G. Mutant studies of yeast phospho- fructokinase. Biochemistry 21, 1935-1942 (1982). Cupp, J. R. & McAlister-Henn, L. Cloning and Characterization of the gene encoding the IDH1 subunit of NAD(+)-dependent isocitrate dehy- drogenase from Saccharomyces cerevisiae. J. Biol. Chem. 267, 16417-16423 (1992). Flikweert, M. T. et al. Pyruvate decarboxylase: an indispensable enzyme for growth of Saccharomyces cerevisiae on glucose. Yeast 12, 247-257 (1996). Gancedo, C. & Delgado, M. A. Isolation and characterization of a mutant from Saccharomyces cerevisiae lacking fructose 1,6-bisphosphatase. Eur. J. Biochem. 139, 651-655 (1984). Gangloff, S. P., Marguet, D. & Lauquin, G. J. Molecular cloning of the yeast mitochondrial aconitase gene (ACO1) and evidence of a synergistic regulation of expression by glucose plus glutamate. Mol Cell Biol 10, 3551-3561 (1990). Hartig, A. et al. Differentially regulated malate synthase genes participate in carbon and nitrogen metabolism of S. cerevisiae. Nucleic Acids Res. 20, 5677-5686 (1992). Heinisch, J. J., Muller, S., Schluter, E., Jacoby, J. & Rodicio, R. Investi- gation of two yeast genes encoding putative isoenzymes of phospho- glycerate mutase. Yeast 14, 203-213 (1998). Kim, K. S., Rosenkrantz, M. S. & Guarente, L. Saccharomyces cerevisiae contains two functional citrate synthase genes. Mol. Cell Biol. 6, 1936- 1942 (1986). Loftus, T. M., Hall, L. V., Anderson, S. L. & McAlister-Henn, L. Iso- lation, characterization, and disruption of the yeast gene encoding cyto- solic NADP-specific isocitrate dehydrogenase. Biochemistry 33, 9661- 9667 (1994). McAlister-Henn, L. & Thompson, L.M. Isolation and expression of the gene encoding yeast mitochondrial malate dehydrogenase. J. Bacteriol. 169, 5157-5166 (1987). Müller, S. & Entian, K.-D. Yeast sugar metabolism. Zimmermann, F. K. & Entian, K.-D. (eds.), pp. 157-170 (Technomic Publishing CO., INC., Lancaster, 1997). Ozcan, S., Freidel, K., Leuker, A. & Ciriacy, M. Glucose uptake and catabolite repression in dominant HTR1 mutants of Saccharomyces cerevisiae. J. Bacteriol. 175, 5520-5528 (1993). Przybyla-Zawislak, B., Dennis, R. A., Zakharkin, S. O. & McCammon, M. T. Genes of succinyl-CoA ligase from Saccharomyces cerevisiae. Eur. J. Biochem. 258, 736-743 (1998). Repetto, B. & Tzagoloff, A. In vivo assembly of yeast mitochondrial alphaketoglutarate dehydrogenase complex. Mol. Cell Biol. 11, 3931- 3939 (1991). Schaaff-Gerstenschlager, I. & Zimmermann, F. K. Pentose-phosphate pathway in Saccharomyces cerevisiae: analysis of deletion mutants for transketolase, transaldolase, and glucose 6-phosphate dehydrogenase. Curr. Genet. 24, 373-376 (1993). Schaaff-Gerstenschlager, I. & Miosga, T. Yeast sugar metabolism. Zimmermann, F. K. & Entian, K.-D. (eds.), pp. 271-284 (Technomic Publishing CO., INC., Lancaster, 1997). Sedivy, J. M. & Fraenkel, D. G. Fructose bisphosphatase of Saccharomyces cerevisiae. Cloning, disruption and regulation of the FBP1 structural gene. J. Mol. Biol. 186, 307-319 (1985). Smith, V., Chou, K. N., Lashkari, D., Botstein, D. & Brown, P. O. Functional analysis of the genes of yeast chromosome V by genetic foot- printing. Science 274, 2069-2074 (1996). Swartz, J. A PURE approach to constructive biology. Nat. Biotechnol. 19, 732-733 (2001). Wills, C. & Melham, T. Pyruvate carboxylase deficiency in yeast: a mutant affecting the interaction between the glyoxylate and Krebs cycles. Arch. Biochem. Biophys. 236, 782-791 (1985).

Throughout this application various publications have been referenced. The disclosures of these publications in their entireties are hereby incorporated by reference in this application in order to more fully describe the state of the art to which this invention pertains.

Although the invention has been described with reference to the examples provided above, it should be understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is only limited by the claims. 

1. A computer readable storage medium or media, comprising: (a) a data structure contained on a computer readable storage medium or media that is read by a computer, said data structure comprising a stoichiometric matrix relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, wherein at least one of said Saccharomyces cerevisiae reactions is annotated to indicate an associated gene, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments; (b) a gene database comprising information characterizing said associated gene; (c) a constraint set for said plurality of Saccharomyces cerevisiae reactions; (d) a program contained on said computer readable storage medium or media comprising executable commands using said data structure for determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes an objective function when said constraint set is applied to said data structure, wherein said at least one flux distribution is predictive of a Saccharomyces cerevisiae physiological function, and (e) said program contained on said computer readable storage medium or media comprising executable commands for visually displaying said at least one resulting flux distribution to a user.
 2. The computer readable storage medium or media of claim 1, wherein said plurality of reactions comprises at least one reaction from a peripheral metabolic pathway.
 3. The computer readable storage medium or media of claim 2, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.
 4. The computer readable storage medium or media of claim 1, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, and consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen.
 5. The computer readable medium or media of claim 1, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of degradation of a protein, degradation of an amino acid, degradation of a purine, degradation of a pyrimidine, degradation of a lipid, degradation of a fatty acid, degradation of a cofactor and degradation of a cell wall component.
 6. The computer readable medium or media of claim 1, wherein said data structure comprises a set of linear algebraic equations.
 7. The computer readable storage medium or media of claim 1, wherein said commands comprise an optimization problem.
 8. The computer readable medium or media of claim 1, wherein said commands comprise a linear program.
 9. The computer readable storage medium or media of claim, wherein a first substrate or product in said plurality of Saccharomyces cerevisiae reactions is assigned to a first compartment and a second substrate or product in said plurality of Saccharomyces cerevisiae reactions is assigned to a second compartment.
 10. The computer readable storage medium or media of claim 1, wherein a plurality of said Saccharomyces cerevisiae reactions is annotated to indicate a plurality of associated genes and wherein said gene database comprises information characterizing said plurality of associated genes.
 11. A computer readable medium or media, comprising: (a) a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) a constraint set for said plurality of Saccharomyces cerevisiae reactions, and (c) commands for determining at least one flux distribution that minimizes or maximizes an objective function when said constraint set is applied to said data representation, wherein said at least one flux distribution is predictive of Saccharomyces cerevisiae growth.
 12. A method for predicting a Saccharomyces cerevisiae physiological function, comprising: (a) storing in a computer a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, wherein at least one of said Saccharomyces cerevisiae reactions is annotated to indicate an associated gene, and wherein a plurality of chemically and electrochemically balanced reactions are assigned to a plurality of different membranous compartments; (b) providing a constraint set for said plurality of Saccharomyces cerevisiae reactions; (c) providing an objective function; (d) executing commands in a suitably programmed computer using said stored data structure for determining at least one flux distribution for said plurality of chemically and electrochemically balanced reactions across said plurality of different membranous compartments that minimizes or maximizes said objective function when said constraint set is applied to said data structure, wherein said at least one flux distribution is predictive of a Saccharomyces cerevisiae physiological function related to said gene, and (e) visually displaying said at least one resulting flux distribution to a user.
 13. The method of claim 12, wherein said plurality of Saccharomyces cerevisiae reactions comprises at least one reaction from a peripheral metabolic pathway.
 14. The method of claim 12, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.
 15. The method of claim 12, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, and consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen.
 16. The method of claim 12, wherein said Saccharomyces cerevisiae physiological function is selected from the group consisting of glycolysis, the TCA cycle, pentose phosphate pathway, respiration, biosynthesis of an amino acid, degradation of an amino acid, biosynthesis of a purine, biosynthesis of a pyrimidine, biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis of a cofactor, metabolism of a cell wall component, transport of a metabolite and metabolism of a carbon source, nitrogen source, oxygen source, phosphate source, hydrogen source or sulfur source.
 17. The method of claim 12, wherein said data structure comprises a set of linear algebraic equations.
 18. The method of claim 12, wherein said data structure comprises a matrix.
 19. The method of claim 12, wherein said flux distribution is determined by linear programming.
 20. The method of claim 12, further comprising: (f) providing a modified data structure, wherein said modified data structure comprises at least one added reaction, compared to the data structure of part (a), and (f) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said modified data structure, thereby predicting a Saccharomyces cerevisiae physiological function.
 21. The method of claim 20, further comprising identifying at least one participant in said at least one added reaction.
 22. The method of claim 21, wherein said identifying at least one participant comprises associating a Saccharomyces cerevisiae protein with said at least one reaction.
 23. The method of claim 22, further comprising identifying at least one gene that encodes said protein.
 24. The method of claim 21, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Saccharomyces cerevisiae physiological function.
 25. The method of claim 12, further comprising: (f) providing a modified data structure, wherein said modified data structure lacks at least one reaction compared to the data structure of part (a), and (g) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said modified data structure, thereby predicting a Saccharomyces cerevisiae physiological function.
 26. The method of claim 25, further comprising identifying at least one participant in said at least one reaction.
 27. The method of claim 26, wherein said identifying at least one participant comprises associating a Saccharomyces cerevisiae protein with said at least one reaction.
 28. The method of claim 27, further comprising identifying at least one gene that encodes said protein that performs said at least one reaction.
 29. The method of claim 26, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Saccharomyces cerevisiae physiological function.
 30. The method of claim 12, further comprising: (f) providing a modified constraint set, wherein said modified constraint set comprises a changed constraint for at least one reaction compared to the constraint for said at least one reaction in the data structure of part (a), and (g) determining at least one flux distribution that minimizes or maximizes said objective function when said modified constraint set is applied to said data structure, thereby predicting a Saccharomyces cerevisiae physiological function.
 31. The method of claim 30, further comprising identifying at least one participant in said at least one reaction.
 32. The method of claim 31, wherein said identifying at least one participant comprises associating a Saccharomyces cerevisiae protein with said at least one reaction.
 33. The method of claim 32, further comprising identifying at least one gene that encodes said protein.
 34. The method of claim 31, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Saccharomyces cerevisiae physiological function.
 35. The method of claim 12, further comprising providing a gene database relating one or more reactions in said data structure with one or more genes or proteins in Saccharomyces cerevisiae.
 36. A method for predicting Saccharomyces cerevisiae growth, comprising: (a) storing in a computer a data structure relating a plurality of Saccharomyces cerevisiae reactants to a plurality of Saccharomyces cerevisiae reactions, wherein each of said Saccharomyces cerevisiae reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) providing a constraint set for said plurality of Saccharomyces cerevisiae reactions; (c) providing an objective function, and (d) executing commands in a suitably programmed computer using said stored data structure to determine at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, thereby predicting Saccharomyces cerevisiae growth. 